Patent Publication Number: US-2013250108-A1

Title: Access Control System by Face Recognition in An Automobile

Description:
RELATED APPLICATIONS 
     This application claims priority to Chinese Patent Application Number 201210074024.2, filed on Mar. 20, 2012 with State Intellectual Property Office of P.R. China (SIPO), which is hereby incorporated by reference. 
     FIELD OF THE INVENTION 
     The present teaching relates to an access control system by face recognition and more particularly to an access control system by face recognition in an automobile. 
     BACKGROUND 
     Face recognition is an emerging identification technology which can automatically identify the identity of a person based on the facial features of the person. Face recognition is carried out by extracting facial feature points of the image from a video based on a widely adopted regional characteristic analysis algorithm integrating with the computer image processing technology, and by further establishing a mathematical model utilizing relevant principles. 
     Generally, face recognition includes image capturing, face location, image preprocessing, face recognition (identification), etc. First, an image of a person is captured by a camera and a facial image is extracted from the captured image. A mathematical model is generated by preprocessing the facial image. Then the generated mathematical model is compared with multiple mathematical models stored in the face database and a similarity value is generated, based on which the identity of the person can be identified. 
     Conventionally, anyone can start an automobile with a key to the automobile, which increases the risk of an automobile being stolen. Furthermore, the owner of an automobile has no way to start it in the case of a key lost. 
     SUMMARY 
     The present teaching relates to an access control system by face recognition and more particularly to an access control system by face recognition in an automobile. 
     In one embodiment, an access control system in an automobile is provided. The access control system includes a grayscale camera, an infrared camera and a processing device. The grayscale camera is configured to capture a grayscale image of the face of a driver. The infrared camera is configured to capture an infrared image of the face of the driver simultaneously with the grayscale camera. The processing device, coupled to the grayscale camera and the infrared camera, includes a processor and a controller. The processor is configured to receive the grayscale image captured by the grayscale camera and the infrared image captured by the infrared camera, convert the grayscale image and the infrared image to a grayscale matrix and an infrared matrix respectively, extract a feature matrix from the grayscale matrix and the infrared matrix, and compute a similarity value by comparing the feature matrix with multiple feature matrices representing facial information of authorized drivers to derive a result of face recognition. The controller is configured to receive the result of face recognition and control a startup device and a warning device of the automobile according to the result of face recognition. 
     In another embodiment, a face recognition system is provided. The face recognition system includes a grayscale camera, an infrared camera and a processor. The grayscale camera is configured to capture a grayscale image of a face. The infrared camera is configured to capture an infrared image of the face simultaneously with the grayscale camera. The processor is configured to receive the grayscale image captured by the grayscale camera and the infrared image captured by the infrared camera, convert the grayscale image and the infrared image to a grayscale matrix and an infrared matrix respectively, extract a feature matrix from the grayscale matrix and the infrared matrix, and compute a similarity value by comparing the feature matrix with multiple feature matrices of predetermined facial information to derive the result of face recognition. 
     In another embodiment, a method of face recognition is provided A grayscale image of a face is captured by a grayscale camera and an infrared image of the face is captured by an infrared camera simultaneously. The grayscale image and the infrared image are sent to a processor. The grayscale image is converted to a grayscale matrix and the infrared image is converted to an infrared matrix by the processor. A feature matrix is extracted from the grayscale matrix and the infrared matrix. A similarity value is computed by comparing the feature matrix with multiple feature matrices by the processor, and a result of face recognition is outputted according to the similarity value. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Features and advantages of embodiments of the claimed subject matter will become apparent as the following detailed description proceeds, and upon reference to the drawings, wherein like numerals depict like parts. These exemplary embodiments are described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings. 
         FIG. 1  illustrates a block diagram of a face recognition system, according to one embodiment of the present disclosure; 
         FIG. 2  illustrates a process of getting a similarity value, according to one embodiment of the present disclosure; 
         FIG. 3  shows a flowchart of operations performed by a face recognition system, according to one embodiment of the present disclosure; 
         FIG. 4  shows an access control system by face recognition in an automobile, according to one embodiment of the present disclosure; 
         FIG. 5  shows an application scenario of an access control system by face recognition in an automobile, according to one embodiment of the present disclosure; and 
         FIG. 6  illustrates a block diagram of an access control system by face recognition in an automobile, according to one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to the embodiments of the present teaching. While the present teaching will be described in conjunction with these embodiments, it will be understood that they are not intended to limit the present teaching to these embodiments. On the contrary, the present teaching is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the present teaching as defined by the appended claims. 
     Furthermore, in the following detailed description of the present teaching, numerous specific details are set forth in order to provide a thorough understanding of the present teaching. However, it will be recognized by one of ordinary skill in the art that the present teaching may be practiced without these specific details. In other instances, well known methods, procedures, components, and circuits have not been described in detail as not to unnecessarily obscure aspects of the present teaching. 
       FIG. 1  illustrates a block diagram of a face recognition system, according to one embodiment of the present disclosure. The face recognition system  10  of the present teaching includes a grayscale camera  11 , an infrared camera  12 , a processor  13  and a feature database unit  14 . 
     In one embodiment, the grayscale camera  11  is configured to capture a grayscale image of the face of a person and send the captured grayscale image to the processor  13 . In the present teaching, by using the grayscale camera  11 , the captured grayscale image can have relatively small image size, therefore, the amount of computation and the storage space in the face recognition system  10  can be effectively reduced without impacting the correctness of the recognition result. Preferably, the grayscale camera  11  captures the grayscale image at a frequency of 2 to 3 frames per second. 
     However, light is essential in the process of capturing image by the grayscale camera  11 . The grayscale of the captured image varies greatly with the variation of light, which can cause errors and affect the result of face recognition. Therefore, according to the present teaching, the face recognition system  10  utilizes the infrared camera  12  to capture an infrared image of the face simultaneously with the grayscale camera  11 . Furthermore, the captured infrared image is sent to the processor  13  for further processing. Preferably, the infrared camera  12  captures the infrared image at a frequency of 2 to 3 frames per second. 
     One of ordinary skill in the art should understand that the infrared image captured by the infrared camera  12  does not rely on the light. Therefore, errors caused by the light can be avoided by sending both the grayscale image captured by the grayscale camera  11  and the infrared image captured by the infrared camera  12  to the processor  13  for processing simultaneously. And the recognition result can be more accurate. 
     The processor  13  receives the grayscale image captured by the grayscale camera  11  and the infrared image captured by the infrared camera  12 , converts each of the grayscale image and the infrared image to a matrix, extracts a feature matrix representing the facial information from the two converted matrices, computes a similarity value with a series of algorithms, and output the result of face recognition according to the similarity value. In one embodiment, the processor  13  obtains the similarity value by comparing the extracted feature matrix with multiple facial information feature matrices stored in the feature database unit  14 . If the similarity value is less than a predetermined threshold, the person is unauthorized. On the other hand, if the similarity value is greater than or equal to the predetermined threshold, the person is authorized. 
     The feature database unit  14 , also known as experience database unit, stores facial information feature matrices, i.e., feature matrices of predetermined facial information. 
       FIG. 2  illustrates a process of getting a similarity value, according to one embodiment of the present disclosure.  FIG. 3  shows a flowchart of operations performed by a face recognition system, in accordance with one embodiment of the present teaching. According to the present teaching, the operations performed by the face recognition system include the procedures of image capturing, image conversion, grayscale correction, matrix processing and result generation. Grayscale correction and matrix processing use algorithms well known to those skilled in the art. The procedures can be implemented by the face recognition system  10  illustrated in  FIG. 1 . And  FIG. 3  is described in combination with  FIG. 1  and  FIG. 2 . 
     In block  301 , the grayscale camera  11  captures a grayscale image  201  of a face, and the infrared camera  12  captures an infrared image  203  of the face simultaneously with the grayscale camera  11 . In one embodiment, both the grayscale camera  11  and the infrared camera  12  capture the facial image at a specified frequency, for example, 2 to 3 frames per second. Both the grayscale image  201  and the infrared image  203  are sent to a processor  13  in the face recognition system  10 . 
     In block  302 , the processor  13  converts the grayscale image  201  captured by the grayscale camera  11  to a grayscale matrix  205 , and converts the infrared image  203  captured by the infrared camera  12  to an infrared matrix  207 . Specifically, each image is divided into N frames, wherein N is an integer and depends on the parameters of the grayscale camera  11  or the infrared camera  12 . For example, N may be in a range from 12 to 36. Three to four frames are selected out of the N frames as the key frames. For example, the selection can be made in terms of a time interval. The image with the selected three to four frames is gray scaled by converting the image to a 16-scale picture, that is, color information of each pixel can be represented by a 4-bit storage unit. Thus, each image is converted to a matrix. For example, a picture with a size of 800*600 can be converted to a matrix with a scale of 800*600, and each element of the matrix takes up a 4-bit storage unit. The process is known as the image conversion. 
     In block  303 , the processor  13  extracts a feature matrix  209  from the grayscale matrix  205  and the infrared matrix  207  with a series of pre-stored algorithms. The feature matrix  209  represents the facial information. More specifically, as described above, the grayscale image  201  is converted to the grayscale matrix  205  and the infrared image  203  is converted to the infrared matrix  207 . And the feature matrix  209  is derived by a characterization processing on the grayscale matrix  205  and the infrared matrix  207 . 
     Advantageously, due to differences in light intensity, the grayscale image  201  captured by the grayscale camera  11  has color aberration compared with the actual image, which can affect the result of face recognition. By characterization processing on the grayscale matrix  205  and the infrared matrix  207 , the impact of light on the image can be minimized since the feature matrix  209  includes information of both an infrared image and a visible image. And a more accurate result of face recognition can be achieved. The process is known as the grayscale correction, which is one of the advantages of the face recognition system and the method thereof according to the present teaching. 
     In block  304 , the processor  13  computes a similarity value  213  by comparing the feature matrix  209  with multiple feature matrices  211  of predetermined facial information pre-stored in the feature database unit  14  The process is known as the matrix processing. 
     In block  305 , the processor  13  outputs the result of face recognition. More specifically, the face is recognized according to the similarity value  213 . In one embodiment, the person with the face is unauthorized if the similarity value  213  is less than a predetermined threshold, and is authorized if the similarity value  213  is greater than or equal to the predetermined threshold. The process is known as result generation. 
     Furthermore, the face recognition system and the method thereof according to the present teaching can be applied to an automobile for automatically identifying the identity of a driver. For example, as shown in  FIG. 4 , a grayscale camera and an infrared camera are installed on a steering wheel of an automobile, a processing device  401  is installed on the plane in the front of the automobile, a startup device  403  is installed besides the steering wheel, and a warning device  405  is installed in a door of the automobile. Though,  FIG. 4  shows a particular location for placement of each device, and the devices can be placed in other locations on the automobile.  FIG. 5  shows an application scenario of an access control system by face recognition in an automobile, according to one embodiment of the present disclosure. 
       FIG. 6  illustrates a block diagram of an access control system by face recognition in an automobile, according to one embodiment of the present disclosure. The face recognition system in  FIG. 1  is included in the access control system in  FIG. 6 . Elements that are labeled the same as in  FIG. 1  and  FIG. 4  have similar functions and will not be repetitively described herein for purposes of brevity and clarity.  FIG. 6  is described in combination with  FIG. 4  and  FIG. 5 . 
     The access control system  601  includes a grayscale camera  11 , an infrared camera  12  and the processing device  401  The processing device  401  further includes a processor  13 , a feature database unit  14  and a controller  603 . The access control system  601  controls the startup device  403  and the warning device  405  by using the face recognition process to identify the identity of a driver of an automobile. 
     The grayscale camera  11  and the infrared camera  12  capture the facial image of the driver simultaneously, immediately after he/she enters the driving cab. The grayscale camera and the infrared camera may be activated by a motion sensor placed inside the automobile. In one embodiment, both the grayscale camera  11  and the infrared camera  12  capture the facial image at a specified frequency, for example, 2 to 3 frames per second. The processor  13  converts the image from the grayscale camera  11  and the image from the infrared camera  12  to a grayscale matrix and an infrared matrix, respectively, extracts a feature matrix from the two converted matrices, and computes a similarity value by comparing the extracted feature matrix with multiple feature matrices representing facial information of authorized drivers which are pre-stored in the feature database unit  14 , so as to derive the result of face recognition and identify the identity of the driver. 
     More specifically, the identity of the driver is identified according to the similarity value. In one embodiment, the driver is unauthorized if the similarity value is less than a predetermined threshold, and is authorized if the similarity value is greater than or equal to the predetermined threshold. The result of face recognition is sent to the controller  601  by the processor  13 . The controller  601  controls the startup device  403  and the warning device  405 , according to the result of face recognition. More specifically, if the driver is authorized, the startup device  403  is enabled by the controller  601  and the automobile can be started by an operation on the startup device  403 . Otherwise, the startup device  403  is disabled by the controller  601 , the automobile cannot be started, and a warning signal is generated by the warning device  405  under the control of the controller  601 . 
     The grayscale camera, the infrared camera, the processing device, the startup device, and the warning device can also be installed in other suitable positions of the automobile. Compared with the conventional access system in an automobile controlled by a key, the access control system by face recognition in one embodiment of the present teaching is much safer and more convenient. With the access control system by face recognition, an automobile can be started without a key, and the risk of the automobile being stolen can be greatly reduced. 
     The access control system in one embodiment of the present teaching can also be used in an anti-theft device, to recognize people who are allowed to drive the automobile and control a warning device to generate a warning signal. The warning signal may be sent as a text message to the user&#39;s mobile device or to a remote monitoring station. 
     While the foregoing description and drawings represent embodiments of the present teaching, it will be understood that various additions, modifications and substitutions may be made therein without departing from the spirit and scope of the principles of the present teaching as defined in the accompanying claims. One skilled in the art will appreciate that the teaching may be used with many modifications of form, structure, arrangement, proportions, materials, elements, and components and otherwise, used in the practice of the teaching, which are particularly adapted to specific environments and operative requirements without departing from the principles of the present teaching. The presently disclosed embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the teaching being indicated by the appended claims and their legal equivalents, and not limited to the foregoing description.