Patent Publication Number: US-2020293756-A1

Title: Frictionless Access Control System Embodying Satellite Cameras for Facial Recognition

Description:
RELATED APPLICATIONS 
     This application claims the benefit under 35 USC 119(e) of U.S. Provisional Application No. 62/577,023, filed on Oct. 25, 2017, which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     Access control systems are often used at public and private premises, such as households, commercial buildings, businesses, retail establishments, schools, hospitals and government buildings, to list a few examples. Access control system nodes are then installed at access points of the premises (e.g. front and interior doors of a building) to control access to restricted areas, such as the building itself or to areas within the buildings. The access control systems authenticate identities of (or authorize) individuals and then permit those authenticated individuals to access the restricted areas through the access point. 
     Historically, the main components of the access control systems were access card readers and possibly door controllers. The access card readers were often installed at the access points and enabled presentation of credentials to obtain access to the restricted areas. Typically, individuals would interact with the access card readers by presenting access cards such as keycards or contactless smart cards to the readers. The access card readers would read the credential information of the keycards and compare the information against a database of authorized individuals to determine if the individuals were authorized to access the restricted areas. If the individuals were authorized, then the access card readers might signal the door controller to unlock doors or not generate alarms, for example. 
     SUMMARY OF THE INVENTION 
     These traditional access control systems have limitations. In one example, the individuals must specifically present the access cards to the card reader at each access point to ingress/egress restricted areas. Individuals typically must place their access cards such that the access cards either make direct physical contact with the access readers or are within a few inches of the access readers. This formal interaction process can be an inconvenience to users of such systems. The access cards can also be stolen, which enables otherwise unauthorized individuals to obtain access. 
     On the other hand, operators of modern access control systems have increasingly incorporated additional security components into the access control systems. These additional components often include surveillance cameras that capture image data, video management systems (VMS) that store the image data, and possibly video analysis systems that analyze the image data. 
     These modern access control systems can even employ image analysis and facial recognition techniques upon the image data to authorize the users. This facial recognition based authorization is in place of or in addition to the key card based authorization. For this purpose, the cameras typically send the image data over the networks (local and/or remote) to the other components of the system for analysis. 
     In general, according to one aspect, the invention features an access control system. The access control system includes one or more surveillance cameras at an access point that capture image data, a facial cropper module, a facial signature module, and a facial recognition module. The facial cropper module extracts facial patches from the image data, and the facial signature module computes facial signatures from the facial patches. The facial recognition module receives the computed facial signatures from the facial signature module, matches the computed facial signatures to stored facial signatures, and sends user identity information of individuals corresponding to the stored facial signatures to the facial signature module when the computed facial signatures match the stored facial signatures. 
     The facial signature module can rank the facial patches using image quality factors to determine acceptable facial patches for individuals, and can compute the facial signatures from the acceptable facial patches. Preferably, the facial signature module determines a highest ranked acceptable facial patch for each of the individuals, and computes a single facial signature for each of the individuals from the highest ranked acceptable facial patch for each of the individuals. 
     Typically, the facial signature module compares each of the facial patches against one another to determine whether the facial patches are associated with same individuals or different individuals. 
     In some embodiments, the surveillance cameras include the facial cropper module and the facial signature module. 
     The access control system might include a local control unit at the access point that includes the facial cropper module and the facial signature module, in other cases. 
     In some cases, the local control unit includes a cache of the stored facial signatures, and the facial recognition module matches the computed facial signatures to the cache of the stored facial signatures. The facial recognition module also executes upon a microcontroller of the local control unit. 
     The access control system can include a connected services system that is remote to the access control point and that includes a server, wherein the server includes the facial recognition module. In these embodiments, the facial recognition module executes upon a microcontroller of the server. 
     In one example, the facial recognition module determines whether the individuals are authorized based on the user identity information. 
     In one implementation, the facial signature module sends a signal to unlock the door system when the facial recognition module determines that the individuals are authorized based upon the user identity information. 
     In one embodiment, the surveillance cameras include the facial cropper module and the facial signature module and send the computed facial signatures to a connected services system that includes the facial recognition module. 
     In general, according to another aspect, the invention features a method for controlling an access control system. This method includes one or more surveillance cameras at an access point capturing image data, extracting facial patches from the image data, computing facial signatures from the facial patches, matching the computed facial signatures to stored facial signatures, and using user identity information of individuals corresponding to the stored facial signatures when the computed facial signatures match the stored facial signatures. 
     The above and other features of the invention including various novel details of construction and combinations of parts, and other advantages, will now be more particularly described with reference to the accompanying drawings and pointed out in the claims. It will be understood that the particular method and device embodying the invention are shown by way of illustration and not as a limitation of the invention. The principles and features of this invention may be employed in various and numerous embodiments without departing from the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the accompanying drawings, reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale; emphasis has instead been placed upon illustrating the principles of the invention. Of the drawings: 
         FIG. 1A  is a schematic, high-level diagram of a distributed access control system to which the invention is applicable, where components of the access control system including cameras, a local control unit, and a connected services system are also shown; 
         FIG. 1B  is a schematic diagram showing a side view of an access point of the access control system in  FIG. 1A ; 
         FIG. 2  is a schematic diagram for a first embodiment of the access control system in  FIG. 1A ; 
         FIG. 3  is a sequence diagram that shows operation of and interactions between various modules in the access control system, where the modules perform image analysis of image data captured by the cameras; 
         FIGS. 4A and 4B  are diagrams that show exemplary frames of the image data from two cameras, where the frames of image data include facial images of an individual at the access point, and where the image data is used to illustrate operation of the access control system; 
         FIG. 5  is a schematic diagram that shows more detail for the connected services system in the first embodiment of the access control system shown in  FIG. 2 ; 
         FIG. 6A  shows more detail for facial recognition request messages and facial recognition response messages used by the access control system during facial recognition to identify individuals; 
         FIG. 6B  shows more detail for user authorization request messages and user authorization response messages used by the access control system, where the access control system uses the messages to determine whether the identified individuals are also authorized users; 
         FIG. 7  is a block diagram that shows more detail for an authorized user table in the connected services system; 
         FIG. 8  is a schematic diagram for a second embodiment of the access control system; 
         FIG. 9  is a schematic diagram for a third embodiment of the access control system; 
         FIG. 10  is a schematic diagram that shows more detail for the local control unit in the third embodiment of the access control system shown in  FIG. 9 ; 
         FIG. 11  is a schematic diagram for a fourth embodiment of the access control system; 
         FIG. 12  is a schematic diagram that shows more detail for the connected services system in the fourth embodiment of the access control system shown in  FIG. 11 ; 
         FIG. 13  is a simplified block diagram showing one way the present access control system could be configured, where the diagram shows a camera that includes facial recognition capabilities connected to a local access controller via a local network, where the camera identifies the individuals and the local access controller determines whether the identified individuals are authorized users; 
         FIG. 14  is a simplified block diagram showing another way the present access control system could be configured, where the diagram shows a local control unit including the facial recognition capabilities and the local access controller connected to the local network; and 
         FIG. 15  is a simplified block diagram showing yet another way the present access control system could be configured, where the diagram shows a connected services system including the facial recognition capabilities and the local access controller. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The invention now will be described more fully hereinafter with reference to the accompanying drawings, in which illustrative embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. 
     As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Further, the singular forms and the articles “a”, “an” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms: includes, comprises, including and/or comprising, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, it will be understood that when an element, including component or subsystem, is referred to and/or shown as being connected or coupled to another element, it can be directly connected or coupled to the other element or intervening elements may be present. 
     Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
       FIG. 1A  shows a distributed access control system  100  to which the invention is applicable. 
     The access control system  100  monitors areas near an access point  10 . The term “access point” refers to a place through which an individual  60  enters or leaves a building or other structure. Examples of access points  10  include doors within or around buildings, elevators, and guard stations at a multi-building facility. 
     In the specific illustrated case, the access point includes a door  30  and a door frame of the door  32 , with a door lock system  20  located between the door  30  and the door frame  32 . The access point  10  is installed in a room  49  of a building. Hinges  63  rotatably connect the door to the door frame. An individual  60  is standing near the door  30 . 
     The access control system has various components. These components include surveillance cameras  103 , a local control unit  18 , a video management system (VMS)  56 , and a connected services system  50 . In the illustrated example, two cameras  103 - 1  and  103 - 2  are mounted to a wall  45  and are located on opposite sides of the door  30 . The placement of the cameras  103  on opposite sides of the door  30  provides both entry and exit access control. 
     Preferably, multiple cameras  103  are placed to capture images of the individuals  60  at the access point  10 . In the illustrated example, two cameras  103 - 1  and  103 - 2  are placed on opposite sides of the access point  10  to improve the chance that facial images of the individuals  60  are captured by at least one of the cameras  103 . The cameras  103 - 1  and  103 - 2  have fields of view  104 - 1  and  104 - 2  that are pointed to capture at least faces of the individuals  60  in image data  70 - 1  and  70 - 2 . The cameras also have lenses which enable the cameras  103  to capture the facial images of the individuals when the individuals  60  are up to 3 meters away from the access point  10 . This allows time for the access control system to perform facial recognition of the individual before the person physically reaches the access point  10 . In one implementation, as many as four cameras  103  are placed at the access point  10 . 
     The cameras  103  might have additional features. In one example, the cameras  103  capture the image data  70  in uncompressed, raw video format. The cameras  103  include an indicator  105  and possibly an intercom  99 . The cameras  103  are typically 1 to 2 mega pixel cameras. If required, higher mega pixel cameras can be used at lower frame rates. 
     The local control unit  18  is a computer system that receives and distributes the image data  70  from the cameras  103  and also controls the door lock system  20 . The local control unit  18  receives image data  70 - 1  and  70 - 2  from cameras  103 - 1  and  103 - 2  via cables  113 - 1  and  113 - 2 . The local control unit can then distribute the image data  70  to other components over local or remote networks. The local control unit  18  sends door unlock signals to the door lock system  20 . 
     The cables  113  are of different types and enable the exchange of different information between the cameras  103  and the local control unit  18 . The cables  113  are typically coaxial cables or shielded twisted pair cables, in examples. Power, image data  70  and control signals are passed through these cables  113 . 
     The local control unit  18  is preferably securely located in proximity to the access point  10 . In the illustrated example, the local control unit  18  is included within a plenum  37  above the access point. The plenum  37  is located between a ceiling  39  of the room  49  and a top of the door frame  32 . 
     The local control unit  18  sends the image data over a local network  123 . The local network  123  might be a local area network or wide area network that supports standard and/or proprietary communications protocols. Example communications protocols include TCP/IP and Ethernet. The local network might be a wired network, or a wireless network using WiMax or WiFi, in examples 
     The VMS  56  is located on the local network  123 . The VMS  56  receives the image data  70  sent from the local control unit  18  and stores the image data  70 . In one example, the cameras  103  stream the image data over the local network  123  to the VMS  56 . Display devices on the network  123  such as monitors can provide multiple live views of the image data  70  via suitable network client connections. 
     The connected services system  50  includes a server  45 , an authorized user table  96  and a facial recognition database  58 . The connected services system  50  is typically cloud based and the server  45  is a low latency cloud server. 
     The connected services system  50  is a remote system that communicates over a leased data connection or private/public network  23  with the local control unit  18 . The connected services system  50  is sometimes administered by separate business entities than the owners and/or occupants of the buildings, which contain the local control unit  18 . In one example, the connected services system  50  can be administered by a third party and/or an entity providing services to the local control unit. In one example, the network  23  is the internet. 
     A facial signature is a file computed for each individual  60  that represents the face of the individual. Each facial signature includes a unique value or unique dataset of values that represent the face of the individual  60 . To compute the facial signature for each individual  60 , a predetermined facial recognition algorithm or transform scans one or more facial images of the individual  60 , and computes the facial signature from the facial images. 
     The facial signatures support different file formats. The formats can be binary or text based. In one specific example, the facial signatures are stored in Common Biometric Exchange File Format (CBEFF). The CBEFF is a standard format for biometric information of individuals such as facial information, and describes a set of data elements necessary to support biometric technologies in a common way. In another specific example, the facial signatures are stored in ISO/IEC 19794-5 format. ISO/IEC 19794-5 is an international format that defines a standard scheme for codifying data describing human faces, within a CBEFF-compliant data structure. In other specific examples, the facial signatures might be stored using any of the following United States American National Standards Institute (ANSI)/National Institute of Standards and Technology (NIST) formats: ANSI/NIST-ITL 1-2007, 1-2011, and 1-2013, ANSI/NIST-ITL 2-2008, and National Information Exchange Model (NIEM). Use of these formats promotes standardized biometric data interchange and interoperability of biometric-based application programs such as the facial recognition algorithms, in examples. Additionally, proprietary formats for storing the facial signatures might be used. 
     The facial recognition database  58  includes entries  89 - 1  . . .  89 -N typically for each individual (e.g. employee, worker) registered with the access control system. In some cases, the database  58  further includes entries for other individuals such as non-employees, visitors and otherwise. Individuals registered with the access control system may or may not be authorized users, however. 
     The entries  89  include stored facial signatures  90  and user identity information  109  for each user, if known. In more detail, the user identity information  109  might include a user name, employee number, visitor badge number, or credential that is unique to each user. In this way, the user identity information  109  in each entry  89  can be used to identify the individual corresponding to the stored facial signature  90 . 
     The authorized user table  96  includes user records  14  for each authorized user. The user records  14  include information for authorizing the users in the facial recognition database  58 . In examples, the user records  14  include information such as the user identity information  109  and an authorization level (e.g. allow or deny access). 
     In general, the individuals  60  are originally registered as users and then authorized when the individuals are hired as employees of a business or other entity at which the access control system  100  is installed. A guard or security operator at the connected services system  50  creates at least one entry  89  in the database  58  for each individual/user. Then, the security operator creates an associated user record  14  for each user/individual in the authorized user table  96 . 
     The stored facial signatures  90  are preferably computed using predetermined facial recognition algorithms or transforms. The predetermined algorithms scan facial images of the individuals  60  and convert the facial images to the facial signatures  90  in response. The algorithms also preferably create the facial signatures  90  in a predetermined or pre-agreed upon file format (e.g. CBEFF or NIEM). 
     The access control system  100  generally operates as follows. The cameras  103  send the image data  70  over the cables  113  to the local control unit  18 . The local control unit forwards the image data  70  over the local network  123  for storage at the VMS  56 . The cameras  103  or the local control unit  18  then perform image analysis on the image data. The image analysis determines facial information of individuals  60  at the access point  10 . Then, either the local control unit or the connected services system  50  perform facial recognition operations upon the facial information. 
     The facial recognition operations determine whether the individuals  60  are identified and authorized users of the access control system. The facial recognition operations first match the facial information determined by the cameras  103  or the local control unit  18  against the facial recognition database  58  to obtain an associated identity for the individuals. The identity information of the individuals are then compared to the authorized user table  96  to determine whether the identified individuals are authorized users of the access control system  100 . 
       FIG. 1B  shows a side view of the access control system  100  in  FIG. 1A . Only camera  103 - 1  is shown. 
     By way of a specific example, two individuals  60 - 1  and  60 - 2  are at the door  30 . Individual  60 - 1  is standing, and individual  60 - 2  is in a wheelchair  31 . The field of view  104 - 1  of the camera  103 - 1  is pointed to enable the camera  103 - 1  to capture facial views of both individuals  60 - 1  and  60 - 2 . 
       FIG. 2  illustrates the first embodiment of the access control system  100 - 1 . 
     This embodiment distributes image analysis and facial recognition between the local control unit  18  and the connected services system  50 . The cameras  103  typically send the image data  70  directly to the local control unit  18  for analysis. For this reason, relatively inexpensive cameras  103  can be used. 
     In the illustrated example, the local control unit  18  includes various components. These components include various interfaces, modules  44 , a microcontroller  22 , an operating system  36 , and a memory  88 . 
     The interfaces include a bidirectional audio interface  142 , a camera interface  122 , a door lock interface  124 , and a network interface  128 . 
     A number of modules  44  are shown. These modules  44  include a facial detector module  144 , a facial cropper module  146 , a facial signature module  148 , and a machine learning and classifier module  149 . The modules might be software or firmware modules. 
     The modules  44 , the operating system  36 , and the microcontroller  22  are shown in a stack-like arrangement. The modules  44  are on top of the operating system  36 , and the operating system  36  is on top of the microcontroller  22 . This arrangement is due to the fact that the operating system  36  operates as an intermediary between the modules  44  and the microcontroller  22 . 
     The operating system  36  facilitates operation of the modules  44  and schedules their execution on the microcontroller  22 . Specifically, the operating system  36  passes information between the microcontroller  22  and the modules  44 , loads instructions of the modules  44  into the memory  88 , and schedules the modules  44  for execution upon the microcontroller  22 . 
     The microcontroller  22  communicates with each of the interfaces to enable the local control unit  18  to communicate and exchange information with other components of the access control system  100 - 1 . Via the cable  113  for each camera  103 , the camera interface  122  receives the image data  70  from the cameras  103 , sends control signals to the cameras  103 , and controls remote power  13  for powering the cameras  103 . The remote power  13  might be based upon alternating current (AC), direct current (DC), power over cable (PoC), or power over Ethernet (PoE/PoE++), in examples. 
     The door lock interface  124  enables the microcontroller  22  to control the door lock system  20 . For this purpose, the microcontroller  22  sends door unlock signals  53  via the door lock interface  124  to the door lock system  20 . The network interface  128  enables communication between the local control unit  18  and the components that connect to local network  123  and the public network  23 . The bidirectional audio interface  142  sends and receives speech signals  199  to/from the intercom  99  of camera  103 - 1 . 
     In the illustrated example, some of the communications associated with the modules  44  are shown. The modules are shown communicating with other modules  44 , the network interface  128 , and the microcontroller  22  via the operating system  36 . A virtual communications channel  76  is shown between the facial signature module  148  and the network interface  128 . This channel  76  abstracts a communications path that includes the facial signature module  148 , the operating system  36 , the microcontroller  22 , and the network interface  128 . 
     The facial signature module  148  and the network interface  128  exchange messages over the virtual communications channel  76 . These messages include facial recognition request messages  33  and facial recognition response messages  43 , and user authorization request messages  73  and user authorization response messages  83 . The facial signature module  148  sends the request messages  33 , 73  via the network interface  128  and public network  23  to the connected services system  50 . In response, the connected services system  50  sends the response messages  43 , 83 . Each facial recognition request message  33  has a corresponding response message  43 , and each user authorization request message  73  has a corresponding response message  83 . 
     The memory  88  also includes image quality factors  93 . During operation of the access control system  100 - 1 , one or more modules  44  access and use the image quality factors  93 . 
     The access control system  100 - 1  generally operates as follows. The camera interface  122  receives the image data  70  from the cameras  103 , and forwards the image data  70  to the microcontroller  22 . The microcontroller  22  and the operating system  36  provide the image data  70  to the modules  44 . The modules  44  detect faces of the individuals  60  in the image data  70  and compute facial signatures from the detected faces. 
     The modules  44  first prepare and send the facial recognition request messages  33  to the connected services system  50 . Specifically, the facial signature module  148  includes the computed facial signatures in the request messages  33 , and sends the request messages  33  via the network interface  128  to the connected services system  50 . 
     The connected services system  50  receives the request messages  33 , determines whether the information in the request messages  33  (i.e. the computed facial signatures) closely match any stored facial signatures  90 , and sends facial recognition response messages  43  back to the local control unit  18 . 
     At the local control unit  18 , the facial signature module  148  creates and sends the user authorization request messages  73  if the content of the facial recognition response messages  43  identifies an individual. Specifically, if the facial recognition response messages  43  include non-NULL user identity information  109  for users, the facial signature module  148  creates the user authorization request messages  73 , includes the user identity information  109  in the request messages  73 , and sends the request messages  73  to the connected services system  50 . If the response messages  43  have NULL references for the user identity information  109 , no request messages  73  are created. 
     The connected services system  50  receives the request messages  73 , and sends user authorization response messages  83  back to the local control unit  18  in response. The response messages  83  indicate whether the individuals represented by the user identity information  109  in the request messages  73  are authorized users. 
     At the local control unit  18 , the facial signature module  148  enables access to the door  30  when the user authorization response messages  83  indicate that the individual  60  is an authorized user and authorized to pass through the door. The local control unit  18  enables access to the door  30  by sending a door unlock signal  53  via the door lock interface  124  to the door lock system  20 . In another implementation, the connected services system  50 , via its server  45 , might send the door lock signal  53  to the local control unit  18 . 
       FIG. 3  shows detailed operation of and interactions between various modules  44  in the access control system. In one example, the modules  44  are located in the local control unit  18  within the first embodiment of the access control system  100 - 1  in  FIG. 2 . These modules  44  include the facial detector module  144 , the facial cropper module  146 , the machine learning and classifier module  149 , and the facial signature module  148 . 
     Operation of these modules  44  is generally as follows. 
     In step  302 , the facial detector module  144  receives frames of image data  70 . In one example, when the facial detector module  144  is located in the local control unit  18  within the first embodiment of the access control system  100 - 1  in  FIG. 2 , the facial detector module  144  receives the image data  70  from the microcontroller  22  of the local control unit  18 . 
     In step  304 , the facial detector module  144  scans the frames of image data  70  and detects faces of individuals  60  in each frame of the image data  70 . Frames that do not include faces of the individuals  60  are dropped. The facial detector module  144  then sends the frames that include the detected faces to the facial cropper module  146  in step  306 . Because the facial detector module  144  drops frames that do not include faces of the individuals  60 , this limits the amount of information exchanged between the modules  144 / 146  and improves processing time. 
     According to step  308 , the facial cropper module  146  identifies and extracts facial patches of the individuals  60  from each received frame. The facial cropper module  146  sends the facial patches to the facial signature module  148  for more analysis. 
     In step  312 , the facial signature module  148  extracts and compares the facial patches across the image data to determine whether the facial patches are associated with same or different individual(s). This operation is performed for the following reason. 
     An individual  60  standing at the access point  10  will be captured on multiple, if not many, frames of image data  70  from possibly multiple cameras  103 . For the purpose of facial recognition, typically only one or several facial patches of an individual  60  are required. Thus, the facial signature module  148  associates detected face patches in one frame with detected face patches in subsequent frames. In this way, though each individual  60  might correspond to a collection of facial patches extracted from multiple frame images, the facial signature module  148  will select only one or several facial patches for each individual  60 . Thus, the facial signature module  148  compares each of the facial patches against one another to determine whether the facial patches are associated with same individuals or different individuals  60 . 
     The facial signature module  148  compares the facial patches against the image quality factors  93  to determine whether the facial patches are suitable for subsequent facial recognition. To improve the likelihood that the facial patches are suitable for subsequent facial recognition, the machine learning and classifier module  149  in step  314  trains a classifier on the image quality factors  93 . The machine learning and classifier module  149  uses machine learning for this purpose. In response to the training, the machine learning and classifier module  149  stores any updates to the image quality factors  93  to the memory  88 . 
     More detail for the image quality factors  93  is as follows. The image quality factors include image blur, motion blur, lighting level, eye detection, and facial pose factors. The image blur factor measures the extent to which the face of an individual  60  in an image, such as the facial patch, is in or out of focus. The motion blur factor measures the extent to which the individual is stationary or moving. The eye detection factor measures the extent to which the individual&#39;s eyes are open and discernible. The lighting level factor measures the extent to which light is directly shone upon an individual&#39;s face, and accounts for shadows and background lighting. The facial pose factor measures the extent to which the face is directly facing a lens of the camera  103 , or tilted or angled relative to the lens. 
     The machine learning and classifier module  149  also trains the classifier on the image quality factors  93  for the following reasons. In one example, a direct correlation between the combination of the image quality factors  93  and facial recognition success is not known. In another example, the facial recognition might be performed at a remote location having possibly different or unknown facial recognition algorithms. These algorithms may be affected differently by the image quality factors  93 . 
     In step  316 , the facial signature module  148  ranks each facial patch. In one implementation, the facial signature module  148  ranks the facial patches by detecting features within each facial patch, and comparing the features against the (now trained) image quality factors  93  in the memory  88 . The features might include eyes, nose, nostrils, eyebrows, ears, lips, and mouth of the individual  60 , scars or other distinguishing features, and possibly facial expressions, in examples. To obtain the overall rank for each facial patch, the facial signature module  148  then might add the values returned from the feature comparisons, in one example. 
     In step  318 , for each facial patch, the facial signature module  148  determines whether the ranked value for each of the facial patches at least meets a threshold acceptable rank value. Facial patches having a ranked value below this threshold are dropped from the analysis, and facial patches having a ranked value that meet the threshold are designated as acceptable facial patches. 
     In step  320 , for each acceptable facial patch for each individual  60 , the facial signature module  148  selects one or more acceptable facial patches having the highest ranking(s). 
     In step  322 , for each individual  60 , the facial signature module  148  computes facial signatures from the acceptable facial patches. When only one highest ranked facial patch was selected in step  320 , the facial signature module  148  computes a single facial signature for that individual  60  from the one highest ranked facial patch. 
     The access control system generally computes the facial signatures of individuals  60  arriving at the access point  10  as follows. The facial signature module  148  computes the facial signatures from the facial patches. For this purpose, the facial signature module  148  preferably uses the same predetermined facial recognition algorithms or transforms that were employed at the connected services system  50  when registering the individuals in the facial recognition database  58 . The facial signature module  148  also computes the facial signatures using the predetermined or pre-agreed upon file format that was employed when creating the facial signatures  90  for the users in the facial recognition database  58 . 
     In one implementation, the facial signature module  148  ranks the facial patches using the image quality factors  93  to determine acceptable facial patches for individuals, and computes the facial signatures from the acceptable facial patches. Moreover, the facial signature module  148  might also determine a highest ranked acceptable facial patch for each of the individuals  60 , and compute a single facial signature for each of the individuals from the highest ranked acceptable facial patch for each of the individuals  60 . 
     According to step  324 , the facial signature module  148  creates a facial recognition request message  33  for each computed facial signature. The facial signature module  148  sends the request message  33  to a facial recognition module, and the facial recognition module sends a facial recognition response message  43  in response. 
       FIGS. 4A and 4B  are diagrams that show exemplary frames of the image data  70 - 1  and  70 - 2  from each of the cameras  103 - 1  and  103 - 2 , respectfully. The image data  70  are used to illustrate operation of various modules  44  in the first embodiment of the access control system  100 - 1  in  FIG. 2 . The image data  70 - 1  and  70 - 2  includes facial images of the same individual  60  at the access point  10 . 
     In  FIG. 4A , the image data  70 - 1  includes frames  70 - 1 - 1  and  70 - 1 - 2 . The frames  70 - 1 - 1  and  70 - 1 - 2  include different side views of the same individual  60 . In  FIG. 4B , the image data  70 - 1  include frames  70 - 2 - 1 ,  70 - 2 - 2 , and  70 - 2 - 3 . The frames  70 - 2 - 1  and  70 - 2 - 2  include different front views of the individual  60 , and frame  70 - 2 - 3  is empty. 
     During operation of the access control system  100 - 1 , the various modules  44  analyze the image data  70  as follows. The facial detector module  144  detects facial images of the individual  60  in frames  70 - 1 - 1  and  70 - 1 - 2  in the image data  70 - 1  of  FIG. 4A , and detects facial images of the individual  60  in frames  70 - 2 - 1  and  70 - 2 - 2  in the image data  70 - 2  of  FIG. 4B . The facial detector module  144  sends the frames  70 - 1 - 1 ,  70 - 1 - 2 ,  70 - 2 - 1 , and  70 - 2 - 2  in which facial images have been detected to the facial cropper module  146 . 
     The facial cropper module  146  identifies and extracts facial patch  80 - 1  from frame  70 - 1 - 1 , facial patch  80 - 2  from frame  70 - 1 - 2 , facial patch  80 - 3  from frame  70 - 2 - 1 , and facial patch  80 - 4  from frame  70 - 2 - 2 . 
     More detail for facial patches  80 - 1  through  80 - 4  is as follows. Facial patch  80 - 1  is a complete side view of the individual&#39;s face and includes both eyes, nose and mouth. Facial patch  80 - 2  is a partial or “cut off” side view of the individual&#39;s face. One of the eyes is missing, and only portions of the nose and mouth exist. Facial patch  80 - 3  is a distinct front view of the individual&#39;s face and includes both eyes, nose and mouth. Facial patch  80 - 4  is a blurred or unevenly lit front view of the individual&#39;s face. The eyes of the individual  60  are not discernible, due to the individual likely blinking when the image was captured. 
     The facial detector module  144  then sends the facial patches  80 - 1 ,  80 - 2 ,  80 - 3 , and  80 - 4  to the facial signature module  148  for further analysis. 
     The facial signature module  148  then ranks the facial patches  80  against the image quality factors  93  to determine acceptable facial patches for the individuals  60 . Here, the facial signature module  148  would likely determine that only facial patch  80 - 1  and  80 - 3  are acceptable facial patches. 
     The facial signature module  148  would likely determine that only facial patch  80 - 1  and  80 - 3  are acceptable facial patches for the following reasons. Facial patches  80 - 1  and  80 - 3  include the entire face of the individual, include facial features such as both eyes, the nose and the mouth. The facial patches  80 - 1  and  80 - 3  are also in focus without motion blur, and were captured under better than average lighting conditions. In contrast, facial patch  80 - 2  does not include the entire face of the individual, and the face is missing an important facial feature such as one of the eyes. Facial patch  80 - 4  is also out of focus, and the eyes of the individual in the facial patch  80 - 4  are closed. As a result, facial patches  80 - 1  and  80 - 3  would likely be acceptable, and patches  80 - 2  and  80 - 4  would be unacceptable. 
     If the facial signature module  148  were to select only one highest ranked acceptable facial patch for the individual  60 , the facial signature module  148  would likely select facial patch  80 - 3 . This is because the facial signature module  148  ranks facial patches having front views of an individual&#39;s face, such as facial patch  80 - 3 , higher than side views such as facial patch  80 - 1 . 
       FIG. 5  shows more detail for the connected services system  50  in the first embodiment of the access control system  100 - 1  in  FIG. 2 . 
     The server  45  of the connected services system  50  includes various components. These components include interfaces, the memory  88 , a server microcontroller  322 , an operating system  136 , and various modules  44 . 
     The interfaces include a public network interface  222  and a database interface  127 . The public network interface  222  enables the connected services system to communicate over the public network  23 , and the database interface  127  enables the server to communicate with the facial recognition database  58  and the authorized user table  96 . 
     The modules  44  include a facial analytics module  52  and a facial recognition module  54 . The modules might be software or firmware modules. 
     The modules  44 , the operating system  136 , and the server microcontroller  322  are shown in a stack-like arrangement. The modules  44  are on top of the operating system  136 , and the operating system  136  is on top of the server microcontroller  322 . This arrangement is due to the fact that the operating system  136  operates as an intermediary between the modules  44  and the server microcontroller  322 . 
     The operating system  136  facilitates operation of the modules  44  and schedules their execution on the microcontroller  322 . Specifically, the operating system  136  passes information between the microcontroller  322  and the modules  44 , loads instructions of the modules  44  into the memory  88 , and schedules the modules  44  for execution upon the server microcontroller  322 . 
     The server microcontroller  22  communicates with each of the interfaces to enable the connected services system  50  to communicate and exchange information with other components. The public network interface  222  forwards the facial recognition request messages  33  and the user authorization request messages  73  received over the public network  23  to the server microcontroller  322 . The server microcontroller  322  sends the facial recognition response messages  43  and the user authorization response messages  83  via the public network interface  222  over the public network  23 . The database interface  127  provides information associated with the facial recognition database  58  and the authorized user table  96  to and from the server microcontroller  322 . 
     In the illustrated example, some of the communications associated with the modules  44  are shown. The modules are shown communicating with other modules  44 , with the database interface  127 , and with the server microcontroller  322  via the operating system  136 . A database virtual communications channel  86  is shown between the facial recognition module  54  and the database interface  127 . This channel  86  abstracts a communications path that includes the facial recognition module  54 , the operating system  136 , the server microcontroller  322 , and the database interface  127 . 
     The connected services system  50  processes the facial recognition request messages  33  generally as follows. The public network interface  222  receives the facial recognition request messages  33  sent from the local control unit  18 , and forwards the request messages  33  to the public network interface  222 . The request messages  33  include the facial signatures  90  of the individuals  60  computed at the access point  10 . The public network interface  222  forwards the facial recognition request messages  33  to the server microcontroller  322 . The server microcontroller  322  provides the request messages  33  to the modules  44  via the operating system  136 . The modules  44  at the server  45  then determine whether the computed facial signatures in the request messages  33  correspond to users registered in the facial recognition database  58 . 
     In more detail, the facial recognition module  54  extracts the computed facial signatures from the request messages  33 , and executes a lookup  85  of the computed facial signatures against the facial recognition database  58 . The facial recognition module  54  sends the lookup messages  85  over the database virtual channel  86  to the facial recognition database  58 . The facial recognition database  58  then compares the facial signatures in the lookup messages  85  against the stored facial signatures  90  in each of the entries  89 - 1  . . .  89 -N. 
     The facial recognition database  58  processes each lookup message  85  as follows. The facial recognition database  58  matches the facial signature in the lookup message  85  against the stored facial signatures  90  in the entries  89 , and computes a match confidence score. 
     The match confidence score is calculated for the following reasons. One reason is that the match is usually not an exact one. This is because although the facial images/patches from which the computed facial signatures  101  and the stored facial signatures  90  might be of the same individual  60 , the facial images are different images, captured at different times and under possibly different conditions. Another reason is that there may be multiple stored facial signatures  90  for the same individual  60 . As a result, the facial recognition database  58  computes the match confidence score to determine which of the stored facial signatures  90  best match the facial signature in the lookup message  85 . 
     If the confidence score for the “best” matching facial signature  90  is below a certain threshold, or no matching signature  90  is found, the facial recognition database  58  returns a NULL entry  89  in response to the lookup  85 . Otherwise, the facial recognition database  58  returns the entry  89  with the closest matching stored facial signature  90 . 
     The facial recognition module  54  then creates the facial recognition response messages  43  from the entries  89  returned in response to the lookup messages  85 . Specifically, if a non-NULL entry  89  is returned for each lookup  85 , the facial recognition module  54  extracts the user identity information  109  from the entry  89  and includes this information in the associated response message  43 . Otherwise, the module  54  includes a NULL reference in the response message  43 . 
     The facial recognition module  54  then sends the response messages  43  over the public network to the local control unit  18 . 
     The connected services system  50  then processes each user authorization request message  73  sent from the local control unit  18  as follows. The facial recognition module  54  extracts user identity information  109  from the request message  73 , and includes the user identity information  109  in a lookup message  95 . The facial recognition module  54  then sends the lookup message  95  over the virtual database channel  86  to the authorized user table  96 . The facial recognition module  54  then determines whether the user identity information  109  (e.g. identifier (ID)) in the lookup message  95  matches a user record  14  in the authorized user table  96 . If such a user record  14  is found, the facial recognition database  58  returns the user record  14  in response to the lookup  95 ; otherwise, the facial recognition database returns a NULL record. 
     The facial recognition module  54  receives the response from the lookup message  95 , and creates an empty user authorization response message  83 . If the lookup  95  returned a non-NULL user record  14 , the facial recognition module  54  sets a Boolean TRUE value within the response message  83 . Otherwise, the facial recognition module  54  sets a Boolean FALSE value. The facial recognition module  54  then sends the response message  83  over the public network  23  to the local control unit  18 . 
     As a result, the access control system includes: one or more surveillance cameras  103  at the access point  10  that capture the image data  70 , the facial cropper module  146 , the facial signature module  148 , and the facial recognition module  54 . The facial cropper module  146  extracts facial patches from the image data  70 , and the facial signature module  148  computes facial signatures from the facial patches. The facial recognition module  54  receives the computed facial signatures from the facial signature module  148 , matches the computed facial signatures to stored facial signatures  90 , and sends user identity information  109  of individuals corresponding to the stored facial signatures to the facial signature module  148  when the computed facial signatures match the stored facial signatures. 
       FIG. 6A  shows more detail for the facial recognition request messages  33  and the facial recognition response messages  43 . Each response message  43  is paired with a corresponding request message  33 . 
     The facial recognition request message  33  has a header  302  and a payload  304  portion. The header  302  includes fields such as a message ID  11 - 1 , a source ID  21 , and a destination ID  23 . In one example, when the access control system uses IP/Ethernet protocols, the IDs  21 / 23  are media access control (MAC) addresses. The payload  304  portion includes the computed facial signature  101  of an individual  60  computed by the facial signature module  148 . 
     The facial recognition response message  43  also has a header  302  and a payload  304  portion. The header  302  includes a message ID  11 - 2 , a request message ID  12 , a source ID  21  and a destination ID  23 . The value of the request message ID  12  is the same value of the message ID  11 - 1  in the corresponding request message  33 . This is indicated by arrow with reference  59 A. In this way, the request and response messages  33 , 43  are paired to one another. 
     In the illustrated example, the payload  304  portion of the response message  43  includes the user identity information  109  of an identified user in the database  58 , such as “john doe, employee # 233 ”. 
     The user identity information  109  corresponds to a stored facial signature  90  in the database  58 . This stored facial signature  90  closely matched the computed facial signature  101  extracted from the corresponding request message  33 . 
       FIG. 6B  shows more detail for the user authorization request messages  73  and the user authorization response messages  83 . Each response message  83  is paired with a corresponding request message  73 . 
     The user authorization request message  73  has a header  302  and a payload  304  portion. The header  302  includes fields such as a message ID  11 - 1 , a source ID  21 , and a destination ID  23 . In the illustrated example, the payload  304  portion includes the same user identity information  109  contents as in the payload  304  of the facial recognition response message  43  in  FIG. 6A . 
     The user authorization response message  83  also has a header  302  and a payload  304  portion. The header  302  includes a message ID  11 - 2 , a request message ID  12 , a source ID  21  and a destination ID  23 . The value of the request message ID  12  is the same value of the message ID  11 - 1  in the corresponding request message  73 . This is indicated by arrow with reference  59 B. In this way, the request and response messages  73 , 83  are paired to one another. 
     The payload  304  portion of the response message  83  includes an indication as to whether the user identity information  109  in the request message  73  was determined to be associated with an authorized user. In one implementation, as shown, the indication is a Boolean value. This value is shown in the payload  304  as “matchFound”  313 . 
       FIG. 7  is a block diagram that shows more detail for the authorized user table  96 . The table  96  includes user records  14  of authorized individuals/users. Exemplary user records  14 - 1  through  14 -N are shown. 
     Each user record  14  typically includes at least the following fields: the user identity information  109 , an authorization level  110 , an employee number  111 , and a supervisor name  112 . 
       FIG. 8  illustrates a second embodiment of the access control system. This second embodiment is indicated using reference  100 - 2 . 
     This embodiment performs the image analysis and facial recognition operations at the local control unit  18 . As in the first embodiment of the access control system  100 - 1  in  FIG. 2 , relatively inexpensive cameras can be used. The local control unit  18  includes a copy of the facial recognition database  58 , in the form of a facial recognition database cache  58 ′. The connected services system  50  is generally not used in this embodiment. 
     The local control unit  18  has similar components as the local control unit  18  in the first embodiment of the access control system  100 - 1  in  FIG. 2 , and includes additional components. The local control unit  18  includes the same interfaces, memory  88 , modules  44 , microcontroller  22 , and operating system  36 . In addition, the local control unit includes a facial recognition database cache  58 ′, an authorized user table cache  96 ′, and additional modules  44 . Specifically, the local control unit  18  additionally includes the modules  44  that are in the connected services system  50  in the first embodiment of the access control system  100 - 1  in  FIG. 5 . These modules are the facial recognition module  54  and the facial analytics module  52 . 
     The facial recognition database cache  58 ′ and the authorized user table cache  96 ′ are typically copies of the facial recognition database  58  and the authorized user table  96  at the connected services system  50 , respectfully. The facial recognition database  58  and the authorized user table  96  can update their respective caches  58 ′/ 96 ′ periodically, or an operator can program the contents of the caches  58 ′/ 96 ′ based on security objectives, in examples. 
     The access control system  100 - 2  generally operates as follows. The cameras  103  send the image data  70  via the cables  113  to the camera interface  122 . The microcontroller  22  obtains the image data  70  from the camera interface  122  and provides the image data for processing by the modules  44 . The facial detector module  144 , the facial cropper module  146 , the facial signature module  148 , and the machine learning and classifier module  149  operate in a substantially similar fashion as in the first embodiment of the access control system  100 - 1  in  FIG. 2 . 
     The facial signature module  148  creates and sends each facial recognition request message  33  to the facial recognition module  54 , and the facial recognition module  54  creates and sends each facial recognition response message  43  in response. In more detail, the facial recognition module  54  receives each request message  33 , extracts the computed facial signature  101  from the request message  33 , and sends the lookup message  85  including the computed facial signature  101  to the facial recognition database cache  58 ′. The cache  58 ′ responds to the lookup  85  by sending the entries  89  having stored facial recognition information  90  that closely matches the computed facial signature  101 , or by sending a NULL entry  89  when no entries match. The facial recognition module  54  then extracts the user identity information  109  from the matching entry  89 , includes the user identity information  109  in a response message  43 , and sends the response message  43  back to the facial signature module  148 . 
     The facial signature module  148  then creates the user authorization request messages  73  if the content of the facial recognition response messages  43  identify individuals (i.e. the payload  304  has non-NULL user identity information  109 ). The facial signature module  148  extracts the user identity information  109  from the facial recognition response messages  43 , and includes this user identity information  109  in the user authorization request messages  73 . 
     The facial signature module  148  then sends the user authorization request messages  73  to the facial recognition module  54 . 
     The facial recognition module  54  then processes each user authorization request message  73  sent from the facial signature module  148 , and creates and sends the user authorization response messages  83  in response. The facial recognition module  54  extracts the user identity information  109  from the request message  73 , and includes the user identity information  109  in a lookup message  95 . The facial recognition module  54  then sends the lookup message  95  to the authorized user table cache  96 ′. The cache  96 ′ then determines whether the user identity information  109  in the lookup message  95  matches a user record  14  in the cache  96 ′. If a matching user record  14  is found, the cache  96 ′ returns the user record  14  in response to the lookup  95 ; otherwise, the cache  96 ′ returns a NULL record. 
     The facial recognition module  54  receives the response from the lookup message  95 , and creates a user authorization response message  83 . If the lookup  95  returned a non-NULL user record  14 , the facial recognition module  54  sets a Boolean TRUE value within the payload  304  of the response message  83 . Otherwise, the facial recognition module  54  sets a Boolean FALSE value. The facial recognition module  54  then sends the response message  83  back to the facial signature module  148 , in response to the associated user authentication request message  73 . 
     If the response messages  83  indicate that the individuals  60  are authorized, the facial signature module  148  sends the door unlock signal  53  via the door lock interface  124  to the door lock system  20 . 
       FIG. 9  illustrates the third embodiment of the access control system. This third embodiment is indicated using reference  100 - 3 . An exemplary camera  103 - 2  within the access control system  100 - 3  is shown. 
     This embodiment distributes image analysis and facial recognition between the cameras  103  and the local control unit  18 . The cameras are “smart” cameras that can analyze the image data  103 . The cameras  103 - 2  analyze the image data  70 , and the local control unit  18  performs the facial recognition. As in the second embodiment of the access control system  100 - 2  in  FIG. 8 , the local control unit  18  includes the facial recognition database cache  58 ′. The connected services system  50  is generally not used in this embodiment. 
     The camera  103 - 2  has various components. These components include an imager  140 , memory  88 , a network interface  228 , a microcontroller  222 , an operating system  236 , and modules  44 . 
     The modules  44  include a frame capture module  142 , the facial detector module  144 , the facial cropper module  146 , the facial signature module  148 , and the machine learning and classifier module  149 . The modules might be software or firmware modules. 
     The modules  44 , the operating system  236 , and the microcontroller  222  are shown in a stack-like arrangement. The modules  44  are on top of the operating system  236 , and the operating system  236  is on top of the microcontroller  222 . This arrangement is due to the fact that the operating system  236  operates as an intermediary between the modules  44  and the microcontroller  222 . 
     The operating system  236  facilitates operation of the modules  44  and schedules their execution on the microcontroller  222 . Specifically, the operating system  236  passes information between the microcontroller  222  and the modules  44 , loads instructions of the modules  44  into the memory  88 , and schedules the modules  44  for execution upon the microcontroller  222 . 
     The microcontroller  222  communicates with the network interface  228  to enable the camera  103 - 2  to communicate and exchange information with the local control unit  18  and the VMS  56 , in examples. 
     The access control system  100 - 3  generally operates as follows. At the camera  103 - 2 , the imager  140  creates an image representation, in pixels, of a scene within the field of view  104 - 2  of the camera  103 - 2 . The imager  140  sends the images via the microcontroller  222  to the frame capture module  142 , which places the images in frames of the image data  70 - 2 . The facial detector module  144 , the facial cropper module  146 , and the facial signature module  148  then analyze the image data  70 - 2  in a substantially similar fashion as in the local control units  18  of the first and second embodiments of the access control systems  100 - 1 / 100 - 2  shown in  FIG. 2  and  FIG. 8 , respectively. 
     The facial signature module  148  creates the request messages  33 , 73  and sends the request messages  33 , 73  via the network interface  228  to the local control unit  18 . The local control unit then  18  creates and sends the response messages  43 , 83  back to the facial signature module  148 . 
     At the facial signature module  148 , if the user authorization response messages  83  indicate that the individuals  60  are authorized, the facial signature module  148  sends the door unlock signal  53  via the door lock interface  124  to the door lock system  20 . 
       FIG. 10  shows more detail for the local control unit  18  in the third embodiment of the access control system  100 - 3 . 
     The local control unit  18  has substantially the same components as the local control units  18  of the first and second embodiments of the access control systems  100 - 1 / 100 - 2  shown in  FIG. 2  and  FIG. 8 . However, the local control unit  18  has a different arrangement of modules  44 . The modules  44  include the facial recognition module  54  and the facial analytics module  52 . 
     The local control unit  18  generally operates as follows. The camera interface  122  receives the request messages  33 , 73  sent over the cable  113 - 2  by the camera  103 - 2 . The microcontroller  22  forwards the request messages  33 , 73  to the modules  44 . The modules  44  perform the facial recognition in a substantially similar fashion as the connected services system  50  in the first embodiment of the access control system  100 - 2  in  FIG. 5 . The facial recognition module  54  creates the response messages  43 , 83  and the microcontroller  22  sends the response messages  43 , 83  via the camera interface  122  and cable  113 - 2  back to the camera  103 - 2 . 
     The facial signature module  148  at the camera  103 - 2  then processes the response messages  43 , 83  to determine whether the individual is both identified and authorized. If the individual is authorized, the camera  103 - 2  sends the door unlock signals  53  over the cable  113 - 2  to the camera interface  122  of the local controller  18 . 
     The microcontroller  22  of the local controller  18  receives the door lock signal from the camera interface  122 , and forwards the door unlock signal  53  via the door lock interface  124  to the door lock system  20 . In another implementation, the facial recognition module  54  at the local control unit  18  can send the door unlock signal  53  upon determining that that the individual  60  is an authorized user. 
       FIG. 11  illustrates the fourth embodiment of the access control system. This fourth embodiment is indicated using reference  100 - 4 . Exemplary camera  103 - 2  within the access control system  100 - 4  is shown. 
     This embodiment distributes image analysis and facial recognition between the cameras  103  and the connected services system  50 . As in the third embodiment of the access control system  100 - 3  in  FIG. 9 , the cameras are “smart” cameras that can analyze the image data  70 . As in the first embodiment of the access control system  100 - 1  in  FIG. 5 , the connected services system  50  performs the facial recognition. There is no local control unit  18  in this embodiment. 
     The access control system  100 - 4  generally operates as follows. The camera  103 - 2  has substantially similar components and operates in a substantially similar manner as the camera  103 - 2  in the third embodiment of the access control system  100 - 3  in  FIG. 9 . The camera  103 - 2  analyzes the image data  70 - 2 , and creates the request messages  33 , 73 . The facial recognition module  148  then sends the request messages  33 , 73  to the connected services system  50 , which performs the facial recognition to identify the individuals, and then determines whether the identified individuals are also authorized users The connected services system  50  creates and sends the response messages  43 , 83  back to the camera  103  in response to the request messages  33 , 73 . 
     The facial signature module  148  receives the response messages  43 , 83  over the network interface  228  from the connected services system  50  and processes the response messages  43 , 83 . If the response messages  43 , 83  indicate that the individuals  60  are identified and authorized, the facial signature module  148  sends the door unlock signal  53  via the door lock interface  124  to the door lock system  20 . 
       FIG. 12  shows more detail for the connected services system  50  in the fourth embodiment of the access control system  100 - 4 . 
     The connected services system  50  has substantially similar components and operates in a substantially similar manner as the connected services system  50  in the first embodiment of the access control system  100 - 1  in  FIG. 5 . As there is no local control unit in this embodiment, the server  45  of the connected services system  50  exchanges the request messages  33 , 73  and the response messages  43 , 83  with the camera  103 - 2 . 
     If the facial recognition module  54  at the connected services system  50  determines that the individual  60  is authorized, the facial recognition module  54  might also send the door unlock signal  53 . The camera  103 - 2  would receive the door lock signal  53  over its network interface  228 , and the microcontroller  22  would forward the door unlock signal  53  via the door lock interface  124  to the door lock system  20 . 
       FIG. 13  is a simplified block diagram showing another way that the access control system could be organized. 
     Here, the camera  103  includes the facial recognition module  54  and the facial recognition database cache  58 ′. The local access controller  118  includes the authorized user table  96 . The camera  103  and the local access controller  118  connect to and communicate over the local network  123 . 
     In operation, the camera  103  executes facial recognition operations to identify users of the access control system. Once the camera  103  has identified the users, the camera  103  sends the information identifying the users over the local network  123  to the local access controller  118 . The local access controller  118  then determines whether the identified users are also authorized users. 
       FIG. 14  is a simplified block diagram showing another way the present access control system could be organized. 
     Here, the local control unit  18  includes the facial recognition module  54  and the facial recognition database cache  58 ′. The local access controller  118  includes the authorized user table  96 . The local control unit  18  and the local access controller  118  connect to and communicate over the local network  123 . The camera  103  connects to the local control unit via cable link  113 . 
     In operation, the local control unit  18  executes facial recognition operations to identify users of the access control system. Once the local control unit  18  has identified the users, the local control unit  18  sends the information identifying the users over the local network  123  to the local access controller  118 . The local access controller  118  then determines whether the identified users are also authorized users. 
       FIG. 15  is a simplified block diagram showing yet another way that the present access control system could be organized. Here, the connected services system  50  includes the facial recognition module  54  and the facial recognition database cache  58 ′. The local access controller  118  includes the authorized user table  96 . 
     The local access controller  118  and the camera  103  are on the local network  123 . The connected services system  50  communicates with the camera  103  and the local access controller  118  via the public network  23 . 
     In operation, the connected services system  50  executes facial recognition operations to identify users of the access control system. Once the connected services system  50  has identified the users, the local control unit  18  sends the information identifying the users over the local network  123  to the local access controller  118 . The local access controller  118  then determines whether the identified users are also authorized users. 
     While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.