Abstract:
A facility access control system and corresponding method are provided. The facility access control system includes a camera configured to capture an input image of a subject attempting to enter or exit a restricted facility. The facility access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The facility access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

Description:
RELATED APPLICATION INFORMATION 
       [0001]    This application claims priority to U.S. Provisional Patent Application Ser. No. 62/365,510, filed on Jul. 22, 2016, incorporated herein by reference herein its entirety. This application claims priority to U.S. Provisional Patent Application Ser. No. 62/366,285, filed on Jul. 25, 2016, incorporated herein by reference herein its entirety. This application is related to an application entitled “Liveness Detection for Antispoof Face Recognition”, having attorney docket number 16015A, and which is incorporated by reference herein in its entirety. This application is related to an application entitled “Camera System for Traffic Enforcement”, having attorney docket number 16015B, and which is incorporated by reference herein in its entirety. This application is related to an application entitled “Physical System Access Control”, having attorney docket number 16015D, and which is incorporated by reference herein in its entirety. This application is related to an application entitled “Login Access Control for Secure/Private Data”, having attorney docket number 16015E, and which is incorporated by reference herein in its entirety. 
     
    
     BACKGROUND 
     Technical Field 
       [0002]    The present invention relates to security and more particularly to liveness detection for antispoof face recognition. 
       Description of the Related Art 
       [0003]    Face recognition has been used to recognize individuals. However, current face recognition systems and approaches are often spoofed using inanimate objects. Thus, there is a need for an improvement to current face recognition systems and approaches that is capable of thwarting spoofing attempts. 
       SUMMARY 
       [0004]    According to an aspect of the present invention, a facility access control system is provided. The facility access control system includes a camera configured to capture an input image of a subject attempting to enter or exit a restricted facility. The facility access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The facility access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task. 
         [0005]    According to another aspect of the present invention, a computer-implemented method is provided for facility access control. The method includes capturing, by a camera, an input image of a subject attempting to enter or exit a restricted facility. The method further includes storing, in a memory, a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The method also includes applying, by a processor, the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject, and wherein the liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task. 
         [0006]    According to yet another aspect of the present invention, a computer program product is provided for facility access control. The computer program product includes a non-transitory computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform a method. The method includes capturing, by a camera, an input image of a subject attempting to enter or exit a restricted facility. The method further includes storing, in a memory, a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The method also includes applying, by a processor, the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task. 
         [0007]    These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0008]    The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein: 
           [0009]      FIG. 1  shows an exemplary system for facility access control, in accordance with an embodiment of the present invention; 
           [0010]      FIG. 2  shows another exemplary system for liveness detection for antispoof face recognition, in accordance with an embodiment of the present principles; 
           [0011]      FIG. 3  shows an exemplary processing system to which the invention principles may be applied, in accordance with an embodiment of the present invention; 
           [0012]      FIG. 4  shows the live image recognition system of  FIG. 2 , in accordance with an embodiment of the present invention; 
           [0013]      FIG. 5  shows an exemplary model architecture for the deep learning engine of  FIG. 4 , in accordance with an embodiment of the present invention; 
           [0014]      FIG. 6  shows an inception structure of the inception layers of  FIG. 5 , in accordance with an embodiment of the present invention; 
           [0015]      FIG. 7  shows a loss structure of the loss layers of  FIG. 5 , in accordance with an embodiment of the present invention; 
           [0016]      FIGS. 8-10  show an exemplary method for liveness detection for face recognition, in accordance with an embodiment of the present invention; 
           [0017]      FIG. 11  shows yet another exemplary system for traffic enforcement, in accordance with an embodiment of the present invention; 
           [0018]      FIG. 12  shows yet another exemplary system for liveness detection for antispoof face recognition, in accordance with an embodiment of the present invention; 
           [0019]      FIG. 13  shows yet another exemplary system for physical system access control, in accordance with an embodiment of the present invention; and 
           [0020]      FIG. 14  shows yet another exemplary system for login access control for secure/private data, in accordance with an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
       [0021]    The present invention is directed to liveness detection for antispoof face recognition. 
         [0022]    The present invention advantageously distinguishes an image of a real face from that of a fake image using liveness detection. The fake image can be implemented, for example, by a high-quality three-dimensional (3D) mask, a paper print-out, an electronic display, or other “distracter” modality. The liveness detection supports face recognition to determine the authenticity of the input and can operate with various hardware. The present invention is hardware independent. 
         [0023]    In an embodiment, a single image with purely appearance-based information can be used to distinguish an image of a real face from the image of a paper print-out of a face image. In an embodiment, one or more deep learning methods can be used that capture both local information (such as, for example, but not limited to, texture) as well as global information (such as, for example, but not limited to, shape and illumination) to provide a multi-task deep learning framework for liveness detection (e.g., using a single image). 
         [0024]    In an embodiment, a deep network structure is set up to specialize from a generic image classification problem to the binary (alive or not alive) classification problem of liveness detection. 
         [0025]    In an embodiment, a multi-task framework is constructed for the liveness detection task along with a large-scale face recognition task. This allows the network to learn better lower level features and improves its generalization ability. 
         [0026]    In an embodiment, data augmentation methods are provided for training the deep learning architecture. The data augmentation methods improve detection performance in multiple scenarios. 
         [0027]    In an embodiment, liveness detection in accordance with the present invention can be implemented on several platforms where face recognition is deployed such as, for example, but not limited to, vehicle electronic channels, automated authentication kiosks (Automated Teller Machines (ATMs), etc.), passenger electronic channels, mobile devices (such as, for example, but not limited to, cellphones and laptops), and so forth. 
         [0028]      FIG. 1  shows an exemplary system  100  for facility access control, in accordance with an embodiment of the present invention. 
         [0029]    The system  100  includes a camera system  110 . While a single camera system  110  is shown in  FIG. 1  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
         [0030]    In the embodiment of  FIG. 1 , the camera system  110  is mounted on a mounting entity  160 . For the sake of illustration, the mounting entity  160  is an arch structure  160  having a controlled gate  161 . The gate  161  can be normally closed and then opened based on a recognition result regarding the person  170  attempting to gain access to the facility. While an arch structure  160  with a gate  161  is shown for the sake of illustration, any other mounting entity can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  110  can be mounted on a building, a pole, and so forth. The preceding examples are merely illustrative. It is to be appreciated that multiple mounting entities can be located at control hubs and sent to a particular location as needed. 
         [0031]    The camera system  110  can be a wireless camera system or can use one or more antennas included on the arch structure  160  (or other mounting entity (e.g., building, drone, etc.) to which the camera system  110  is mounted or proximate). 
         [0032]    The system  100  further includes a server  120  for liveness detection for antispoof face recognition. The server  120  can located remote from, or proximate to, the camera system  110 . The server  120  includes a processor  121 , a memory  122 , and a wireless transceiver  123 . The processor  121  and the memory  122  of the remove server  120  are configured to perform liveness detection for antispoof face recognition based on images received from the camera system  110  by the (the wireless transceiver  123  of) the remote server  120 . To that end, the processor  121  and memory  122  can be configured to include components of a face recognition system and a live image recognition system. In this way, the face of a person  170  in a vehicle  180  can be recognized and a liveness of the person  170  can also be determined for the purpose of facility access control. The face recognition and liveness detection can be performed on multiple vehicle occupants including the driver. Moreover, persons walking up to the arch structure  160  (i.e., without being in a vehicle) can also be recognized and so forth in accordance with the present invention. 
         [0033]    Accordingly, some exemplary suitable environments to which the present invention can be applied can include any environments a person must be recognized in order to gain access to a facility. Moreover, further exemplary suitable environments can include any environments where liveness detection can be used to augment a face recognition result for facility access control. 
         [0034]      FIG. 2  shows another exemplary system  200  for liveness detection for antispoof face recognition, in accordance with an embodiment of the present principles. System  200  can represent an implementation of system  100 , in accordance with an embodiment of the present invention. 
         [0035]    The system  200  includes a live image recognition system  220 , a face recognition system  230 , and a computer processing system  240 . 
         [0036]    The computer processing system  240  essentially controls the other systems of system  200 , including the image recognition system  220  and the face recognition system  230 . 
         [0037]    The live image recognition system  220  recognizes live images. The image recognition system  220  can, for example, detect an image of a live object versus an image of an inanimate object that is used for spoofing. 
         [0038]    The face recognition system  230  recognizes faces. According, the face recognition system  230  can also recognize individuals associated with recognized faces. 
         [0039]    The computer processing system  240  performs functions relating, for example, but not limited to: (i) processing image recognition results; (ii) processing face recognition result (iii) performing one or more actions based on the image recognition results and/or the face recognition results. 
         [0040]    The live image recognition system  220  and/or the face recognition system  230  and/or the computer processing system  240  can include a camera. For the sake of illustration, a separate camera  271  is shown. The camera  271  can capture images/video of the user. The images/video can then be processed by the computer processing system  240  and/or any of the systems under its control. In an embodiment, the camera  271  can be capable of capturing still and moving images. In an embodiment, the camera can acquire images in the visible and/or infra-red spectrum. 
         [0041]    The live image recognition system  220  and/or the face recognition system  230  and/or the computer processing system  240  can include a display. For the sake of illustration, a separate display  272  is shown. The display  272  can show liveness detection (live image) information and/or face recognition information to the user or other personnel (e.g., security personnel). For example, the display  272  can indicate to a security guard that someone is attempting to spook a face recognition system to allow the security guard to intervene. 
         [0042]    The live image recognition system  220  and/or the face recognition system  230  and/or the computer processing system  240  can include a speaker. For the sake of illustration, a separate speaker  273  is shown. The speaker  273  can provide auditory guidance to the user (e.g., regarding face positioning, and so forth) and/or a local alert that someone is attempting to spoof the system in order to draw attention to that person. 
         [0043]    In an embodiment, one or more of the elements of system  200  is implemented in the cloud using a cloud-based configuration. 
         [0044]    In the embodiment shown in  FIG. 2 , the elements thereof are interconnected by a bus(es)/network(s)  201 . However, in other embodiments, other types of connections can also be used. Moreover, in an embodiment, at least one of the elements of system  200  is processor-based. 
         [0045]    Further, while one or more elements may be shown as separate elements, in other embodiments, these elements can be combined as one element. For example, while the camera  211  is shown as a separate entity from the face recognition system  230 , in other embodiments the face recognition system  230  can include the camera, obviating the need for a separate camera. Also for example, while the live image recognition system  220  is shown as a separate entity from the face recognition system  230 , in other embodiments the live image recognition system  220  can be included in the face recognition system. Additionally for example, while the live image recognition system  220 , the face recognition system  230 , and the computer processing system  240  are shown as separate entities, in other embodiments, the live image recognition system  220  and the face recognition system  230  can be included in the computer processing system  240 . The converse is also applicable, where while one or more elements may be part of another element, in other embodiments, the one or more elements may be implemented as standalone elements. 
         [0046]    Moreover, one or more elements of  FIG. 2  can be implemented in a cloud configuration including, for example, in a distributed configuration. 
         [0047]    Additionally, one or more elements in  FIG. 2  may be implemented by a variety of devices, which include but are not limited to, Digital Signal Processing (DSP) circuits, programmable processors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), Complex Programmable Logic Devices (CPLDs), and so forth. 
         [0048]    These and other variations of the elements of system  200  are readily determined by one of ordinary skill in the art, given the teachings of the present principles provided herein, while maintaining the spirit of the present principles. 
         [0049]      FIG. 3  shows an exemplary processing system  300  to which the invention principles may be applied, in accordance with an embodiment of the present invention. The processing system  300  can be used, for example, to implement the server  120  of  FIG. 1 , the overall system  200  or the computer processing system  240  of  FIG. 2 . 
         [0050]    The processing system  300  includes at least one processor (CPU)  304  operatively coupled to other components via a system bus  302 . A cache  306 , a Read Only Memory (ROM)  308 , a Random Access Memory (RAM)  310 , an input/output (I/O) adapter  320 , a sound adapter  330 , a network adapter  340 , a user interface adapter  350 , and a display adapter  360 , are operatively coupled to the system bus  302 . 
         [0051]    A first storage device  322  and a second storage device  324  are operatively coupled to system bus  302  by the I/O adapter  320 . The storage devices  322  and  324  can be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid state magnetic device, and so forth. The storage devices  322  and  324  can be the same type of storage device or different types of storage devices. 
         [0052]    A speaker  332  is operatively coupled to system bus  302  by the sound adapter  330 . A transceiver  342  is operatively coupled to system bus  302  by network adapter  340 . A display device  362  is operatively coupled to system bus  302  by display adapter  360 . 
         [0053]    A first user input device  352 , a second user input device  354 , and a third user input device  356  are operatively coupled to system bus  302  by user interface adapter  350 . The user input devices  352 ,  354 , and  356  can be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, and so forth. Of course, other types of input devices can also be used, while maintaining the spirit of the present invention. The user input devices  352 ,  354 , and  356  can be the same type of user input device or different types of user input devices. The user input devices  352 ,  354 , and  356  are used to input and output information to and from system  300 . 
         [0054]    Of course, the processing system  300  may also include other elements (not shown), as readily contemplated by one of skill in the art, as well as omit certain elements. For example, various other input devices and/or output devices can be included in processing system  300 , depending upon the particular implementation of the same, as readily understood by one of ordinary skill in the art. For example, various types of wireless and/or wired input and/or output devices can be used. Moreover, additional processors, controllers, memories, and so forth, in various configurations can also be utilized as readily appreciated by one of ordinary skill in the art. These and other variations of the processing system  300  are readily contemplated by one of ordinary skill in the art given the teachings of the present invention provided herein. 
         [0055]      FIG. 4  shows the live image recognition system  220  of  FIG. 2 , in accordance with an embodiment of the present invention. 
         [0056]    The live image recognition system  220  includes a data collector  401 , a data collector  402 , a data collector  403 , a deep learning engine  410 , and application related data  420 . 
         [0057]    The data collector  401  is for collecting 3D mask data  401 A for 3D masks that are used as potential inputs to a face recognition system. 
         [0058]    The data collector  402  is for collecting printed paper data  402 A for printed papers that are used as potential inputs to a face recognition system. 
         [0059]    The data collector  403  is for collecting electronic display data  403 A for electronic displays that are used as potential inputs to a face recognition system. 
         [0060]    Regarding data collectors  401 ,  402 , and  403 , 3D masks, 2D printed paper, electronic devices with displays, respectively, are used as spoofing material for face recognition. In an embodiment, a large variety of data samples, which are commonly used in various applications/scenarios, is collected. In an embodiment, the collected data is preferably provided by more than one person, e.g., wearing 3D masks with different accessories (such as, for example, but not limited to, wigs, scarf, glasses, and so forth). In an embodiment, the collected data preferably includes pictures taken using different cameras like Closed Circuit Television (CCTV) cameras, web cameras, mobile device cameras, industrial grade cameras, and so forth. The 2D printed paper and electronic devices also presented in front of the cameras to collect training data. In an embodiment, the collected data involves persons with different nationalities and different postures to collect diverse training data. 
         [0061]    The deep learning based engine  410  includes a training portion  410 A and an architecture portion  410 B. The deep learning based engine  410  performs deep learning for liveness detection for face recognition. 
         [0062]    Regarding the architecture portion  410 B of the deep learning based engine  410 , in an embodiment, a multi-task learning objective is set up to augment liveness detection with a large-scale face recognition task. In an embodiment, features at the lower layers in the deep learning architecture are shared between these two tasks. 
         [0063]    Further regarding the architecture portion  410 B of the deep learning based engine  410 , in an embodiment, data augmentations in the form of chromatic transformations and image contrast enhancements can be used, in addition to spatial transformations. 
         [0064]    Regarding the training portion  410 A of the deep learning based engine  410 , in an embodiment, training is achieved by fine tuning the GoogLeNet architecture on a multitask objective that jointly optimizes liveness detection and face recognition. This allows overcoming the challenge of limited data for liveness detection by using large-scale face recognition datasets. 
         [0065]    Further regarding the architecture portion  410 B of the deep learning based engine  410 , the architecture differs from a standard GoogLeNet by setting up multi-task learning on top of the original structure, which improves the generalization ability of our model. 
         [0066]    Further regarding the training portion  410 A of the deep learning based engine  410 , in an embodiment, all of the layers are frozen except for the last fully connected layer. Then the two tasks of liveness detection and face recognition are combined (jointly optimized) to conduct the multi-task training. During the multi-task training, all weights of the layers are unfrozen. 
         [0067]    Regarding the deep learning based engine  410 , data preprocessing is achieved by applying face detection to the data (input images). Then, in an embodiment, the input images are normalized to 256 by 256. By further random central cropping, the final output to the network is 228 by 228. In order to increase the diversity of the training data and improve the model&#39;s ability of generalization, a data augmentation layer right after the image input. The image has a fifty percent probability to be horizontally flipped, zoomed, translated or rotated. Also, in an embodiment, an image contrast enhancement is applied by randomly adding one or more Gaussian distributed instances to the original RGB value. Additionally, in an embodiment, another image contrast enhancement is applied by adding one or more Gaussian distributed instances to the HSV domain and multiplying some coefficients to further adjust the S and V. 
         [0068]    The application related data  420  can include data relating to vehicle based applications  420 A, kiosk based applications  420 B, electronic channel based applications  420 C, mobile device based applications  420 D, and Personal Computer (PC) based applications  420 E. It is to be appreciated that the preceding applications are merely illustrative and, thus, the present invention can be applied to many other applications, as readily determined by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. 
         [0069]    Regarding vehicle based applications  420 A, the liveness detection engine can be implemented on a novel platform for vehicle automatic clearance channels, which can be used for automated authentication at highways, weighing stations and border control locations. 
         [0070]    Regarding the kiosk based applications  420 B, the liveness detection engine can be implemented on a novel platform for automated authentication kiosks, which can be used for access control at airports and so forth. 
         [0071]    Regarding the electronic channel based applications  420 C, the liveness detection engine can be implemented on a novel platform for passenger automatic clearance channels, which can be used for automated entry authentication into secure areas, such as immigration at airports or at border control locations. 
         [0072]    Regarding the mobile device applications  420 D, the liveness detection engine can be implemented on mobile platforms such as cellphones and laptops, for supporting face recognition in secure access, mobile payments, banking and similar applications, and so forth. 
         [0073]    Regarding the Personal Computer (PC) applications  420 E, the liveness detection engine can be implemented on PC platforms such as desktop and laptops, for supporting face recognition in secure access, mobile payments, banking and similar applications, and so forth. 
         [0074]      FIG. 5  shows an exemplary model architecture  500  for the deep learning engine  410  of  FIG. 4 , in accordance with an embodiment of the present invention. 
         [0075]    The model architecture  500  includes a convolutional max-pooling Local Response Normalization (LRN) layer  505 , a convolutional max-pooling Local Response Normalization (LRN) layer  510 , an inception×4 layer  515 , an inception×3 layer  520 , an inceptionby2 layer  525 , a Fully Connected (FC) layer  530 , a loss layer  535 , a FC layer  540 , a FC layer  545 , a loss layer  550 , and a loss layer  555 . 
         [0076]    The convolutional max-pooling LRN layers  505  and  510  partition an input image into a set of non-overlapping rectangles and, for each such sub-region, outputs the maximum. The intuition is that the exact location of a feature is less important than its rough location relative to other features. The convolutional max-pooling LRN layers  505  and  510  serve to progressively reduce the spatial size of the representation, to reduce the number of parameters and amount of computation in the network, and hence to also control overfitting. 
         [0077]    The convolution layer (e.g., layer  510 ) takes in the original image as input and send output to the inception layers  515 ,  520  and  525 , which further generates the output for the fully connected (FC) layer  535 ,  540 , and  545 . The output of FC layers predict the classification label and penalized by the loss layers  530 ,  550 , and  555 , which conduct back-propagation for the whole structure to update. 
         [0078]    The high-level reasoning in the neural network is performed using the FC layers  530 ,  540 , and  545 . Neurons in a fully connected layer have full connections to all activations in the previous layer. Accordingly, their activations can be computed with a matrix multiplication followed by a bias offset. 
         [0079]    The loss layers  535 ,  550 , and  555  specify how training penalizes the deviation between the predicted and true labels. Various loss functions appropriate can be used, while maintaining the spirit of the present invention. 
         [0080]      FIG. 6  shows an inception structure  600  of the inception layers  515 ,  520 , and  525  of  FIG. 5 , in accordance with an embodiment of the present invention. 
         [0081]    The inception structure  600  includes a depth concatenate element  601 , a convolutional 1×1 (Conv 1×1) element  611 , a convolutional 1×1 (Conv 1×1) element  612 , a Max pooling 3×3 element  613 , a convolutional 1×1 (Conv 1×1) element  621 , a convolutional 5×5 (Conv 5×5) element  622 , a convolutional 1×1 (Conv 1×1) element  623 , and a depth concatenate element  631 . 
         [0082]      FIG. 7  shows a loss structure  700  of the loss layers  505  and  510  of  FIG. 5 , in accordance with an embodiment of the present invention. 
         [0083]    The loss structure  700  includes a Fully Connected (FC) layer  701 , a liveness FC layer  711 , a face recognition FC layer  712 , a liveness penalty element  721 , and a face recognition penalty element  722 . 
         [0084]      FIGS. 8-9  show an exemplary method  800  for liveness detection for face recognition, in accordance with an embodiment of the present invention. Steps  805  through  815  can be considered to correspond to a training stage  800 A of method  800 , while steps  820  through  825  can be considered to correspond to a recognition stage  800 B of method  800 , and step  830  can be considered to correspond to a post-recognition stage  800 C. 
         [0085]    At step  805 , provide images of various types of spoofing materials used for spoofing a face recognition system. The various types of spoofing materials can include, for example, but are not limited to, images of 3D masks, images printed on paper, and images depicted on electronic displays. 
         [0086]    At step  810 , perform data preprocessing on the images. The data preprocessing can involve, for example, but is not limited to, image contrast enhancements, normalization, cropping (e.g., random central cropping, etc.), data augmentation, and so forth. 
         [0087]    In an embodiment, step  810  includes one or more of steps  810 A,  810 B,  810 C, and  810 D. 
         [0088]    At step  810 A, apply an image contrast enhancement to the images by randomly adding one or more Gaussian distributed instances to an original RGB value in a RGB color space. The original image is changed to another image, which shows the variance in the RGB color space for each pixel. This changed image showing certain variance from the original input is served as the network input, which artificially increase the diversity of the input images. 
         [0089]    At step  810 B, apply an image contrast enhancement to the images by adding one or more Gaussian distributed instances to a Hue, Saturation, and Value (HSV) domain (cylindrical-coordinate representations of the RGB color space) and multiply some Gaussian distributed coefficients to further adjust S and V (brightness). By doing so, The Hue, Saturation and Value (brightness) are changed to create sufficient diversity for the input of the network. 
         [0090]    At step  810 C, normalize the image (e.g., regarding size or some other characteristic). 
         [0091]    At step  810 D, perform cropping on the images. In an embodiment, random central cropping can be performed on the images. 
         [0092]    At step  815 , train a liveness-based face recognition system using the images preprocessed by step  810  to form a deep learning based model. Step  815  can involve training the deep learning based engine. 
         [0093]    In an embodiment, step  815  includes one or more of steps  815 A and  815 B. 
         [0094]    At step  815 A, freeze all layers except for the last fully connected layer. 
         [0095]    At step  815 B, perform multi-task training involving a liveness detection task and a face recognition task. In an embodiment, all of the weights of the layers can be unfrozen during the multi-task training. 
         [0096]    At step  820 , receive an input image of a person to be recognized. 
         [0097]    At step  825 , perform a multi-task operation jointly involving liveness detection and face recognition for a particular application, using the deep learning based model. The particular application can include, but is not limited to, a vehicle based application, a kiosk based application, an electronic channel based application, a mobile device application, and a personal computer application. 
         [0098]    At step  830 , perform one or more actions based on a result of the multi-task operation. For example, the one or more actions can be directed to one or more of the following: identifying the person and also determining that the person is live; traffic enforcement; facility access control; physical system (e.g., workplace machine) access control; and login access control (e.g., to secure/private data). 
         [0099]    In an embodiment, step  830  can include one or more of steps  830 A through  830 E. 
         [0100]    At step  830 A, corresponding to identifying the person and also determining that the person is live, perform one or more actions that can include, but are not limited to, one or more of the following: permitting or blocking a purchase of an item; generating local and/or remote alarm; and so forth. 
         [0101]    At step  830 B, corresponding to traffic enforcement, perform one or more actions that can include, but are not limited to, one or more of the following: forwarding the input image of the one or more subjects in the motor vehicle to a corresponding traffic enforcement authority, responsive to the motor vehicle committing a traffic violation; forwarding the input image, that depicts a particular one of the one or more subjects that is located in an operating location in the motor vehicle, to a corresponding traffic enforcement authority, responsive to a liveness determination of another one of the one or more subjects in the motor vehicle being determined as not live and a location of the motor vehicle being in a High Occupancy Vehicle (HOV) lane; forwarding the input image, that depicts a particular one of the one or more subjects that is located in an operating location in the motor vehicle and another one of the one or more subjects located in a non-operating position in the motor vehicle, to a corresponding traffic enforcement authority, responsive to a liveness determination of the other one of the one or more subjects being determined as not live and a location of the motor vehicle being in a HOV lane; forwarding an image of the vehicle operator (when a liveness of a passenger “object” has been determined to be “not live”); capturing a more expansive image of the user (who should be proximate to the spoofing object) in order to capture the users&#39; actual face (or license plate in the case of the user utilizing a fake passenger while driving in the HOV lane) and forwarding that expansive image to a corresponding traffic enforcement authority. It is to be appreciated that the initially listed action can encompass the actions that follow. 
         [0102]    At step  830 C, corresponding to facility access control, the one or more actions can include, but are not limited to, one or more of the following: permitting or restricting access to an object and/or a facility; locking a door or area to keep someone out or to keep someone in; generating local and/or remote alarm (e.g., indicative of a security breach of the facility); and so forth. 
         [0103]    At step  830 D, corresponding to physical system (e.g., workplace machine) access control, the one or more actions can include, but are not limited to, one or more of the following: powering down or powering up the workplace machine (e.g., the controlled system, machine, and/or device or a portion thereof); locking or unlocking a physical or electronic lock that blocks operation of the workplace machine (e.g., the controlled system, machine, and/or device or a portion thereof); generating local and/or remote alarm (e.g., indicative of unauthorized attempted use of the workplace machine); and so forth. 
         [0104]    At step  830 E, corresponding to login access control (e.g., to secure/private data), the one or more actions can include, but are not limited to, one or more of the following: permitting or restricting access (e.g., to secure/private data); generating local and/or remote alarm (e.g., indicative of unauthorized attempted access to, e.g., secure/private data); and so forth. Access restriction can be achieved using, for example, a switch or other physical element to prevent access to the secure data. 
         [0105]    As is evident to one of ordinary skill in the art, the action(s) taken is(are) dependent upon the type of application to which the present invention is applied. 
         [0106]      FIG. 11  shows yet another exemplary system  1100  for traffic enforcement, in accordance with an embodiment of the present invention. 
         [0107]    The system  1100  includes a camera system  1110 . While a single camera system  1110  is shown in  FIG. 11  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
         [0108]    In the embodiment of  FIG. 11 , the camera system  1110  is mounted on a mounting entity  1160 . For the sake of illustration, the mounting entity  1160  is a pole  1160 . The pole  1160  is also used to support a stop sign. Of course, a dedicated pole or other mounting entity could also be used. Thus, while a pole  1160  is shown for the sake of illustration, any other mounting entity can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  1110  can be mounted on a building, a drone, and so forth. The preceding examples are merely illustrative. It is to be appreciated that multiple mounting entities can be located at control hubs and sent to a particular location as needed. 
         [0109]    The camera system  1110  can be a wireless camera system or can use one or more antennas included on the pole  1160  (or other mounting entity (e.g., building, drone, etc.) to which the camera system  1110  is mounted or proximate). 
         [0110]    The system  1100  further includes a server  1120  for traffic enforcement. The server  1120  can be configured to perform traffic enforcement based on liveness detection for antispoof face recognition. The server  1120  can located remote from, or proximate to, the camera system  1110 . The server  1120  includes a processor  1121 , a memory  1122 , and a wireless transceiver  1123 . The processor  1121  and the memory  1122  of the remove server  1120  are configured to perform liveness detection for antispoof face recognition based on images received from the camera system  1110  by the (the wireless transceiver  1123  of) the remote server  1120 . To that end, the processor  1121  and memory  1122  can be configured to include components of a face recognition system and a live image recognition system. In this way, the face of a person  1170  in a vehicle  1180  can be recognized and a liveness of the person  1170  can also be determined. Accordingly, a vehicle driver can be positively identified and associated with a traffic offense, while the state of liveness of a “passenger object” (false person such as, for example, a mannequin), if present, can also be identified and associated with a traffic offense (violation of the number of persons allowed in a High Occupancy Vehicle (HOV) lane) for the vehicle driver. 
         [0111]    Accordingly, some exemplary suitable environments to which the present invention can be applied can include any environments a face recognition result can be associated with traffic enforcement. Moreover, further exemplary suitable environments can include any environments where liveness detection can be used to augment a face recognition result for traffic enforcement. 
         [0112]      FIG. 12  shows yet another exemplary system  1200  for liveness detection for antispoof face recognition, in accordance with an embodiment of the present invention. 
         [0113]    The system  1200  includes a camera system  1210 . While a single camera system  1210  is shown in  FIG. 12  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
         [0114]    In the embodiment of  FIG. 12 , the camera system  1210  is mounted on a mounting entity  1260 . For the sake of illustration, the mounting entity  1260  is a pole  1260 . While a pole  1260  is shown for the sake of illustration, any other mounting entity can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  1210  can be mounted on a building, a drone, and so forth. The preceding examples are merely illustrative. It is to be appreciated that multiple mounting entities can be located at control hubs and sent to a particular location as needed. 
         [0115]    The camera system  1210  can be a wireless camera system or can use one or more antennas included on the pole  1260  (or other mounting entity (e.g., building, drone, etc.) to which the camera system  1210  is mounted or proximate). 
         [0116]    The system  1200  further includes a server  1220  for liveness detection for antispoof face recognition. The server  1220  can located remote from, or proximate to, the camera system  1210 . The server  1220  includes a processor  1221 , a memory  1222 , and a wireless transceiver  1223 . The processor  1221  and the memory  1222  of the remove server  1220  are configured to perform liveness detection for antispoof face recognition based on images received from the camera system  1210  by the (the wireless transceiver  1223  of) the remote server  1220 . To that end, the processor  1221  and memory  1222  can be configured to include components of a face recognition system and a live image recognition system. In this way, the face of a person  1270  in a vehicle  1280  can be recognized and a liveness of the person  1270  can also be determined. 
         [0117]    Accordingly, some exemplary suitable environments to which the present invention can be applied can include any environments where liveness detection can be used to augment a face recognition result. For example, a mass transit hub or stop, a battlefield, and/or any place where potential causalities can occur and where liveness detection can be used to advantageously augment a face recognition result (e.g., in order to determine a number of injured, a number of non-injured, a ratio of injured versus non-injured, etc.). 
         [0118]      FIG. 13  shows yet another exemplary system  1300  for physical system access control, in accordance with an embodiment of the present invention. The system  1300  can be used to control access to a workplace machine. 
         [0119]    The system  1300  includes a camera system  1310 . While a single camera system  1310  is shown in  FIG. 13  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
         [0120]    In the embodiment of  FIG. 13 , the camera system  1310  is mounted on a mounting entity  1360 . For the sake of illustration, the mounting entity  1360  involves a section of a wall (hereinafter “wall section”)  1360  and a mounting bracket  1361 . While a wall section  1360  and a mounting bracket  1361  are shown for the sake of illustration, any other mounting entity(ies) can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  1310  can be mounted on a pole, and so forth. The preceding examples are merely illustrative. It is to be appreciated that multiple mounting entities can be located at control hubs and sent to a particular location as needed. 
         [0121]    The camera system  1310  can be a wireless camera system or can use one or more antennas included on the wall section  1360  and/or the mounting bracket  1361  (or other mounting entity (e.g., building, pole, drone, etc.) to which the camera system  1310  is mounted or proximate). 
         [0122]    The system  1300  further includes a server  1320  for liveness detection for antispoof face recognition for permitting workplace machine use. The server  1320  can located remote from, or proximate to, the camera system  1310 . The server  1320  includes a processor  1321 , a memory  1322 , and a wireless transceiver  1323 . The processor  1321  and the memory  1322  of the remove server  1320  are configured to perform liveness detection for antispoof face recognition based on images received from the camera system  1310  by the (the wireless transceiver  1323  of) the remote server  1320 . To that end, the processor  1321  and memory  1322  can be configured to include components of a face recognition system and a live image recognition system. In this way, the face of a person  1370  that is to operate a workplace machine  1390  can be recognized and a liveness of the person  1370  can also be determined. Here, liveness can be correlated to attentiveness, in that a sleeping person can be considered to lack liveness, therefore resulting in an alarm or other action being performed in order to prevent the inattentive employee from operating a workplace machine  1390 . The face recognition and liveness detection can be performed on multiple operators of multiple workplace machines  1390 . 
         [0123]    Accordingly, some exemplary suitable environments to which the present invention can be applied can include any environments a person must be recognized in order to gain access to a workplace machine. Moreover, further exemplary suitable environments can include any environments where liveness detection can be used to augment a face recognition result for workplace machine use control. 
         [0124]      FIG. 14  shows yet another exemplary system  1400  for login access control for secure/private data, in accordance with an embodiment of the present invention. 
         [0125]    The system  1400  includes a camera system  1410 . While a single camera system  1410  is shown in  FIG. 14  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
         [0126]    In the embodiment of  FIG. 14 , the camera system  1410  is embodied in a laptop  1460 . Hence, the laptop can be considered a mounting entity for the camera system  1410 . While a laptop  1460  is shown for the sake of illustration, any other mounting entity(ies) can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  1410  can be mounted on a pole, and so forth. The preceding examples are merely illustrative. It is to be appreciated that multiple mounting entities can be located at control hubs and sent to a particular location as needed. 
         [0127]    The camera system  1410  can communicate with the server  1420  using a wireless transceiver  1410 A of the laptop  1410 . 
         [0128]    The system  1400  further includes a server  1420  for liveness detection for antispoof face recognition for controlled access to secure/private data on a server  1490 . The server  1420  can located remote from, or proximate to, the camera system  1410 . The server  1420  includes a processor  1421 , a memory  1422 , and a wireless transceiver  1423 . The processor  1421  and the memory  1422  of the remove server  1520  are configured to perform liveness detection for antispoof face recognition based on images received from the camera system  1410  by the (the wireless transceiver  1423  of) the remote server  1420 . To that end, the processor  1421  and memory  1422  can be configured to include components of a face recognition system and a live image recognition system. In this way, the face of a person  1470  that is to login to a network (e.g., network  1480 ) or device (e.g., server  1490 ) can be recognized and a liveness of the person  1470  can also be determined in order to control access to the secure/private data. 
         [0129]    The system  1400  can be used to control access to the server  1490  through computer network  1480 . Access control is performed by the server  1420 . The elements shown relative to  FIG. 14  are set forth for the sake of illustration. However, it is to be appreciated that the present invention can be applied to other network configurations and other operational environments as readily contemplated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. 
         [0130]    A description will now be given of some of the many attendant advantages of the present invention, in accordance with one or more embodiments of the present invention. 
         [0131]    In an embodiment, the present invention uses a single image, thus reducing complexity of the acquisition system compared to the prior art. 
         [0132]    In an embodiment, the present invention achieves very high accuracy and negligible false alarms, thus providing a higher quality of the result than previous methods. 
         [0133]    In an embodiment, a purely image-based system is used, thus reducing the cost of the system compared to using active sensors. 
         [0134]    In an embodiment, the robustness of the present invention is higher than prior art solutions, since passive imaging is not sensitive to deployment configurations. 
         [0135]    In an embodiment, the present invention can achieve very high generality, thus being capable of being used to support any application where face recognition is deployed. In an embodiment, no additional hardware is required, as the present invention can be incorporated into an existing face recognition system. 
         [0136]    Embodiments described herein may be entirely hardware, entirely software or including both hardware and software elements. In a preferred embodiment, the present invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. 
         [0137]    Embodiments may include a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. A computer-usable or computer readable medium may include any apparatus that stores, communicates, propagates, or transports the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be magnetic, optical, electronic, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. The medium may include a computer-readable storage medium such as a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk, etc. 
         [0138]    Each computer program may be tangibly stored in a machine-readable storage media or device (e.g., program memory or magnetic disk) readable by a general or special purpose programmable computer, for configuring and controlling operation of a computer when the storage media or device is read by the computer to perform the procedures described herein. The inventive system may also be considered to be embodied in a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein. 
         [0139]    A data processing system suitable for storing and/or executing program code may include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code to reduce the number of times code is retrieved from bulk storage during execution. Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I/O controllers. 
         [0140]    Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters. 
         [0141]    The foregoing is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the Detailed Description, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that those skilled in the art may implement various modifications without departing from the scope and spirit of the invention. Those skilled in the art could implement various other feature combinations without departing from the scope and spirit of the invention. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.