CN101059838A - Human face recognition system and recognition method - Google Patents
Human face recognition system and recognition method Download PDFInfo
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Abstract
The invention relates to a face recognize system, comprising a network interface module, a dual-core chip SOC module connected with the network interface module, a CPLD address code, a DDR2 high-speed operation memory, and an external program memory, or even with a hard disk with an IDE. The dual-core chip module is connected with the high-speed operation memory via a data bus. The CPLD address code via an EMIF bus is connected with the external program memory NAND Flash or connected with the IDE hard disc too. And the whole system is powered by a power module. The recognize method comprises video code compression, pretreatment, motion check, face positioning, organ positioning, normalizing, character extraction, face comparison, and data communication. The invention has the advantages in high recognize rate and high recognize speed, strong data processing ability, better compatibility with prior detect network, stable operation, easy upgrade and maintenance, with low cost.
Description
Technical field:
The present invention relates to a kind of face identification system and recognition methods, it is the special-purpose embedded technology of from network flow media data, carrying out recognition of face automatically, belong to electronics automatic identification technology field, be specially adapted to network monitoring system that intelligence is had relatively high expectations, as: the urban public security monitoring and alarming system uses, confirm identity by recognition of face, also can it be used as video monitoring system that record is also preserved video record in occasions such as customs, airports.
Technical background:
Computer face identification is meant based on known people's face sample storehouse, utilizes Computer Analysis image and mode identification technology from static state or dynamic scene, discerns or verify one or more people's faces.Usually position, yardstick and the attitude information that the available essential information in back comprises people's face handled in identification.Utilize Feature Extraction Technology also can further extract more biological characteristic (as: race, sex, age).Computer face identification is the previous very active subject of order, and it can be widely used in important events such as security system, criminal's identification and proof of identification.Though it is human very strong for the recognition capability of people's face, can remember and thousands of different people's faces of identification, then difficulty is how for computing machine, it shows: human face expression is abundant, people's face changes with the growth at age, the influence that decorations such as hair style, beard, glasses cause people's face, image that people's face becomes are subjected to that illumination, imaging angle and image-forming range etc. influence etc.
Existing people's face automatic identification technology is based on PC mostly before the present invention proposes, and the work of recognition of face all is that the identification software of giving on the PC is finished.It is to have utilized Embedded framework that part is also arranged, but whole structure do not change, and is from video camera and obtains image, and the pattern of handling then can't be carried out remote monitoring and be handled.And this series products has cost height, poor stability, be difficult to upgrading and safeguard and be difficult to shortcomings such as compatibility with existing monitor network.
Summary of the invention:
An object of the present invention is: the shortcoming that overcomes prior art, a kind of face identification system is provided, it can carry out recognition of face to the video flowing that comes automatic network, have sufficiently high discrimination and high recognition speed, with strong data-handling capacity, with existing monitor network can fine compatibility, and working stability, be easy to upgrading and maintenance, cost is low.
The technical scheme of a kind of face identification system of the present invention is: a kind of face identification system, and it has:
A Network Interface Module, it is used for receiving the video flowing or the figure of automatic network;
A double-core chip SOC module is used for the video streaming image and the figure that receive from network are detected, and judges wherein whether contain people's face information, and this people's face is discerned;
Described Network Interface Module is connected with double-core chip SOC module;
The address decoding of a CPLD is used to manage the peripherals of double-core chip SOC module;
A DDR2 high-speed computation internal memory is as the internal memory of double-core chip SOC module;
An external program memory NAND Flash is used for storage and detects and recognizer, or has the hard disk of an IDE simultaneously, is used to realize the high capacity access of local data;
Above-mentioned double-core chip SOC module is connected by the DDR2 data bus with DDR2 high-speed computation internal memory; The address decoding of CPLD is connected with external program memory NAND Flash by the EMIF bus, or is connected with the IDE hard disk simultaneously; What provide power supply for total system is power module.
The further technical scheme that has additional technical feature on the basis of such scheme is:
Described face identification system, its Network Interface Module, its employing standard is 10M/100M, directly web camera on LAN (Local Area Network) or the Internet or DVR is managed and visits by network, or by the WEB webpage system is managed with parameter work is set.
Described face identification system, its double-core chip SOC module be selected from TI company double-core chip ARM9+DM64X SOC module, be the SOC (system on a chip) module; This double-core chip SOC module comprises an ARM9 control chip and the dsp chip that special image is handled, and has comprised high-speed internal L2 cache chip simultaneously.
Described face identification system, its DDR2 high-speed computation internal memory is the high speed DDR2 SDRAM that has expanded according to the needs of practical application, be used to realize high-speed computation, in the DSP kernel, realize specific people's face algorithm, and transplanted (SuSE) Linux OS in another ARM9 kernel, be used to realize the function of network and system management.
The processing that described face identification system, its external program memory NAND Flash have the NARD FLASH program space storer of a 64MB to be used to solidify code and to realize local small data.
Described face identification system, its external program memory NAND Flash also has the hard disk of an IDE, the high capacity access that is used to solidify code and realizes local data.
A kind of face identification system obvious technical effects of the present invention, integrated multiple technologies such as recognition of face, network data base, portrait combination, video image acquisition and processing, obtain the long-distance video image information by network, compare fast with the portrait in the database, reach in time and determine by the purpose of discriminating person's true identity.Possess accurate computing method and strong data-handling capacity, thereby have sufficiently high discrimination and high recognition speed; The application of this system is very extensive, and being open to the custom with the airport is example, and the passenger is when handling the safety check formality, and its face image will be taken from different perspectives by the multiple cameras that system was equipped with, and afterwards, the picture that collects will be back to server; Compare by the facial image that will collect and the facial image in the database, airport security department just can discern the person's of being open to the custom " true features " in short 1 second, and can be transferred to remote general headquarters' monitoring management, even if the person of being identified has adopted easily appearance means such as wears glasses, stickup beard, system also can judge exactly.
Another object of the present invention is, a kind of recognition methods of face identification system is provided, by optimizer, realize that people's face detection speed is fast, accuracy in detection is high, with the purpose of the good stable performance of legacy network equipment compatibility.
The recognition methods technical scheme of a kind of face identification system of the present invention is: it comprises the steps:
A, compressed video decoder:
Stream medium data to Network Transmission carries out compressed video decoder, obtains digital image sequence;
B, pre-service:
Original image to input comprises gray processing, the illumination compensation pre-service, and the quality of raising image obtains gray scale image;
C, motion detection:
Adopt frame difference method and mixed Gaussian background modeling to define motion jointly the gray level image of input, or do not have the motion generation; All then think have motion to take place when the prospect connected region that two kinds of methods detect,, then carry out follow-up people's face and detect not have the motion generation, then do not carry out follow-up people's face and detect, and check if detect if detected the motion generation greater than threshold value;
D, people's face location:
Front face is detected in real time, determine the position of people's face in image; Comprise little feature calculation unit and sorter unit; Described little feature calculation unit is that gray level image to be detected is carried out convergent-divergent, and exhaustive search candidate face window calculates the microstructure features of each window, and it passed to AdaBoost neural network classifier unit adjudicate;
E, organ location:
The organ location is to determine the position of people's face in image, comprises the location of eyes, two eyebrow, nose, face, lower jaw; Face shape facility and AdaBoost sorter according to people's face the zone of detected people's face window carry out eyes, two eyebrow, nose, face, lower jaw location to C in the step, for potential pseudo-organ, adopt the discrimination principle of maximum a posteriori probability to carry out filtering;
F, normalization:
According to the positional information of organ, try to achieve normalized gray level image, it is that image is comprised rotation, convergent-divergent, shearing manipulation, makes the eyes level, the height of lower jaw is certain;
G, feature extraction:
From whole people's face, extract the face component feature, comprise naked face, eyebrow, eyes, nose, mouth face component; Utilize principal component method to extract the eigenwert of face component;
H, the comparison of people's face:
Draw human face similarity degree: it is in known face database people's face to be identified to be adopted the calculating similarity and carries out multimodal overall recognition of face and local recognition of face by the method for sequencing of similarity;
I, data communication:
To detect or recognition result sends to server by network.
The further technical scheme that has additional technical feature on the basis of such scheme is:
The recognition methods of described face identification system, its stream medium data) carrying out the compressed video decoder support comprises MPEG2, MPEG4, H.263, H.264 all kinds of video formats.
The recognition methods of described face identification system, its pre-service are the facial images that obtains by network interface, facial image is carried out submitting to after the digitizing in the DM6446 program on the dsp chip carry out Flame Image Process.
The recognition methods of described face identification system extracts face component and is characterized as five kinds of parts from whole people's face; Utilize principal component method to extract the eigenwert of five kinds of parts.
The effect of the recognition methods of face identification system of the present invention is: decoding speed is fast, the efficient height; Pre-service, motion detection, people's face and organ alignment quality height, accurate, information is transmitted unimpeded, and speed is fast.
Description of drawings:
Fig. 1: the hardware block diagram of face identification system of the present invention.
Fig. 2: the application block schematic of face identification system of the present invention.
Fig. 3: the treatment scheme sketch of face identification system of the present invention.
Embodiment
1, in conjunction with the accompanying drawings 1 and embodiment be described further as follows to a kind of face identification system of the present invention:
Embodiment 1: it has a Network Interface Module 1, is used for receiving the video flowing or the figure of automatic network; A double-core chip SOC module 2 is used for the video streaming image and the figure that receive from network are detected, and judges wherein whether contain people's face information, and this people's face is discerned; Described Network Interface Module 1 is connected with double-core chip SOC module 2; The address decoding 3 of a CPLD is used to manage the peripherals of double-core chip SOC module 2; A DDR2 high-speed computation internal memory 4 is as the internal memory of double-core chip SOC module 2; An external program memory NAND Flash5 is used for storage and detects and recognizer, has the hard disk 6 of an IDE simultaneously, is used to realize the high capacity access of local data; Above-mentioned double-core chip SOC module 2 and DDR2 high-speed computation internal memory 4 are connected by the DDR2 data bus; The address decoding 3 of CPLD is connected with external program memory NAND Flash 5 by the EMIF bus, is connected with IDE hard disk 6 simultaneously; What provide power supply for total system is power module 7; Described Network Interface Module 1, its employing standard is 10M/100M, directly web camera on LAN (Local Area Network) or the Internet or DVR are managed and visit by network, also can manage with parameter system work is set by the WEB webpage; Described double-core chip SOC module 2 be selected from TI company double-core chip ARM9+DM64X SOC module, be the SOC (system on a chip) module; This double-core chip SOC module 2 has an ARM9 control chip and the dsp chip that special image is handled, and has high-speed internal L2 cache chip simultaneously; Described DDR2 high-speed computation internal memory 4 is the high speed DDR2 SDRAM that expanded according to the needs of practical application, be used to realize high-speed computation, in the DSP kernel, realize specific people's face algorithm, and transplanted (SuSE) Linux OS in another ARM9 kernel, be used to realize the function of network and system management; DDR2 high-speed computation internal memory 4 adopts the high-speed procedure storer of the NAND of 64MB, and when system powered on, the program that is stored in above the FALSH was loaded into execution in the high-speed procedure storer, can satisfy the needs of system's full speed running; Described external program memory NAND Flash 5 has the NARD FLASH program space storer of a 64MB to be used to solidify code, also has the hard disk 6 of an IDE, the high capacity access that is used to solidify code and realizes local data.
Embodiment 2: different with the foregoing description is: be not with hard disk, realize the processing of local small data, detect people's face in this locality and extract eigenwert, and the extraction eigenwert that will extract and people's face normalized image are passed to monitoring central server, realize the alignment algorithm part at server end, this design is suitable for the contrast and the identification of large nuber of images database data.
2, in conjunction with the accompanying drawings 2,3 and embodiment be described further as follows to the recognition methods of a kind of face identification system of the present invention:
A kind of recognition methods that is used for face identification system, its step is as follows:
A, compressed video decoder: the stream medium data to Network Transmission carries out compressed video decoder, obtains digital image sequence; Input: stream medium data; Output: the digital picture behind the decompress(ion);
B, pre-service: the original image to input comprises gray processing, the illumination compensation pre-service, and the quality of raising image obtains gray scale image; Input: the digital picture behind the decompress(ion); Output: gray level image;
C, motion detection: adopt frame difference method and mixed Gaussian background modeling to define motion jointly the gray level image of input, or do not have the motion generation; All then think have motion to take place when the prospect connected region that two kinds of methods detect,, then carry out follow-up people's face and detect not have the motion generation, then do not carry out follow-up people's face and detect, and check if detect if detected the motion generation greater than threshold value; Input: the gray level image of adjacent two frames; Output: motion detection result;
D, people's face location: front face is detected in real time, determine the position of people's face in image; Comprise little feature calculation unit and sorter unit; Described little feature calculation unit is that gray level image to be detected is carried out convergent-divergent, and exhaustive search candidate face window calculates the microstructure features of each window, and it passed to AdaBoost neural network classifier unit adjudicate; Input: gray level image; Output: the position of people's face window in image;
E, organ location: the organ location is to determine the position of people's face in image, comprises the location of eyes, two eyebrow, nose, face, lower jaw; Face shape facility and AdaBoost sorter according to people's face the zone of detected people's face window carry out eyes, two eyebrow, nose, face, lower jaw location to C in the step, for potential pseudo-organ, adopt the discrimination principle of maximum a posteriori probability to carry out filtering; Input: gray level image, people's face position of window; Output: the position of organ in image;
F, normalization: according to the positional information of organ, try to achieve normalized gray level image, it is that image is comprised rotation, convergent-divergent, shearing manipulation, makes the eyes level, and the height of lower jaw is certain; Input: gray level image, the organ position in image; Output: normalized gray level image
G, feature extraction: from whole people's face, extract the face component feature, comprise naked face, eyebrow, eyes, nose, mouth face component; Utilize principal component method to extract the eigenwert of face component; Input: normalized gray level image, the organ position in image; Output: component feature;
H, the comparison of people's face: draw human face similarity degree: it is in known face database people's face to be identified to be adopted the calculating similarity and carries out multimodal overall recognition of face and local recognition of face by the method for sequencing of similarity; Input: the component feature of image to be detected, the component feature of the sample in the database; Output: similarity;
I, data communication: will detect or recognition result sends to server by network; Input: Control on Communication signal; Output: TCP/IP packet;
Above-mentioned stream medium data carries out the compressed video decoder support and comprises MPEG2, MPEG4, H.263, H.264 all kinds of video formats; Described pre-service is the facial image that obtains by network interface, facial image is carried out submitting to after the digitizing in the DM6446 program on the dsp chip carry out Flame Image Process; The described face component that extracts from whole people's face is characterized as five kinds of parts; Utilize principal component method to extract the eigenwert of five kinds of parts.
Protection scope of the present invention is not limited to the foregoing description.
Claims (10)
1, a kind of face identification system is characterized in that, it comprises:
A Network Interface Module (1), it is used for receiving the video flowing or the figure of automatic network;
A double-core chip SOC module (2) is used for the video streaming image and the figure that receive from network are detected, and judges wherein whether contain people's face information, and this people's face is discerned;
Described Network Interface Module (1) is connected with double-core chip SOC module (2);
The address decoding of a CPLD (3) is used to manage the peripherals of double-core chip SOC module (2);
A DDR2 high-speed computation internal memory (4) is as the internal memory of double-core chip SOC module (2);
An external program memory NAND Flash (5) is used for storage and detects and recognizer, or has the hard disk (6) of an IDE simultaneously, is used to realize the high capacity access of local data;
Above-mentioned double-core chip SOC module (2) is connected by the DDR2 data bus with DDR2 high-speed computation internal memory (4); The address decoding of CPLD (3) is connected with external program memory NAND Flash (5) by the EMIF bus, or is connected with IDE hard disk (6) simultaneously; What provide power supply for total system is power module (7).
2, face identification system according to claim 1, it is characterized in that, described Network Interface Module (1), its employing standard is 10M/100M, directly web camera on LAN (Local Area Network) or the Internet or DVR are managed and visit by network, or by the WEB webpage system is managed with parameter work is set.
3, face identification system according to claim 1 is characterized in that, described double-core chip SOC module (2) be selected from TI company double-core chip ARM9+DM64X SOC module, be the SOC (system on a chip) module; This double-core chip SOC module (2) comprises an ARM9 control chip and the dsp chip that special image is handled, and has comprised high-speed internal L2 cache chip simultaneously.
4, face identification system according to claim 1, it is characterized in that, described DDR2 high-speed computation internal memory (4) is the high speed DDR2 SDRAM that has expanded according to the needs of practical application, be used to realize high-speed computation, in the DSP kernel, realize specific people's face algorithm, and transplanted (SuSE) Linux OS in another ARM9 kernel, be used to realize the function of network and system management.
5, face identification system according to claim 1 is characterized in that, the processing that described external program memory NAND Flash (5) has the NARD FLASH program space storer of a 64MB to be used to solidify code and to realize local small data.
6, face identification system according to claim 1 or 5 is characterized in that, described external program memory NAND Flash (5) also has the hard disk (6) of an IDE, the high capacity access that is used to solidify code and realizes local data.
7, a kind of recognition methods that is used for the described face identification system of claim 1 is characterized in that it comprises the steps:
A, compressed video decoder:
Stream medium data to Network Transmission carries out compressed video decoder, obtains digital image sequence;
B, pre-service:
Original image to input comprises gray processing, the illumination compensation pre-service, and the quality of raising image obtains gray scale image;
C, motion detection:
Adopt frame difference method and mixed Gaussian background modeling to define motion jointly the gray level image of input, or do not have the motion generation; All then think have motion to take place when the prospect connected region that two kinds of methods detect,, then carry out follow-up people's face and detect not have the motion generation, then do not carry out follow-up people's face and detect, and check if detect if detected the motion generation greater than threshold value;
D, people's face location:
Front face is detected in real time, determine the position of people's face in image; Comprise little feature calculation unit and sorter unit; Described little feature calculation unit is that gray level image to be detected is carried out convergent-divergent, and exhaustive search candidate face window calculates the microstructure features of each window, and it passed to AdaBoost neural network classifier unit adjudicate;
E, organ location:
The organ location is to determine the position of people's face in image, comprises the location of eyes, two eyebrow, nose, face, lower jaw; Face shape facility and AdaBoost sorter according to people's face carry out eyes, two eyebrow, nose, face, lower jaw location to the zone of detected people's face window in (C), for potential pseudo-organ, adopt the discrimination principle of maximum a posteriori probability to carry out filtering;
F, normalization:
According to the positional information of organ, try to achieve normalized gray level image, it is that image is comprised rotation, convergent-divergent, shearing manipulation, makes the eyes level, the height of lower jaw is certain;
G, feature extraction:
From whole people's face, extract the face component feature, comprise naked face, eyebrow, eyes, nose, mouth face component; Utilize principal component method to extract the eigenwert of face component;
H, the comparison of people's face:
Draw human face similarity degree: it is in known face database people's face to be identified to be adopted the calculating similarity and carries out multimodal overall recognition of face and local recognition of face by the method for sequencing of similarity;
I, data communication:
To detect or recognition result sends to server by network.
8, the recognition methods of face identification system according to claim 7 is characterized in that, described stream medium data carries out the compressed video decoder support and comprises MPEG2, MPEG4, H.263, H.264 all kinds of video formats.
9, the recognition methods of face identification system according to claim 7, it is characterized in that, described pre-service is the facial image that obtains by network interface, facial image is carried out submitting to after the digitizing in the DM6446 program on the dsp chip carry out Flame Image Process.
10, the recognition methods of face identification system according to claim 7 is characterized in that, the described face component that extracts from whole people's face is characterized as five kinds of parts; Utilize principal component method to extract the eigenwert of five kinds of parts.
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