CN106682607A - Offline face recognition system and offline face recognition method based on low-power-consumption embedded and infrared triggering - Google Patents
Offline face recognition system and offline face recognition method based on low-power-consumption embedded and infrared triggering Download PDFInfo
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- CN106682607A CN106682607A CN201611206197.XA CN201611206197A CN106682607A CN 106682607 A CN106682607 A CN 106682607A CN 201611206197 A CN201611206197 A CN 201611206197A CN 106682607 A CN106682607 A CN 106682607A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/94—Hardware or software architectures specially adapted for image or video understanding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/40—Spoof detection, e.g. liveness detection
- G06V40/45—Detection of the body part being alive
Abstract
The invention discloses an offline face recognition system and an offline face recognition method based on low-power-consumption embedded and infrared triggering. The offline face recognition method comprises the steps of continuously detecting an object distance, and returning the object distance by means of a voltage value manner; determining whether an object enters a recognition range according to the returned voltage value, acquiring an image of the image which enters the recognition range, determining whether the acquired image is a face image, if not, displaying error information, and if yes, determining whether the current face image belongs to a moving object, if not, displaying the error information; and if yes, performing matching on the face image and stored information in a face characteristic database, and outputting a matching result. The offline face recognition system and the offline face recognition method have advantages of ultralow power consumption, high mute stability, small size, high convenience in mounting, offline recognition, normal operation without networking, high stability and high reliability in operation.
Description
Technical field
The present invention relates to a kind of offline face identification system and method based on low-power-consumption embedded and infrared triggering.
Background technology
Contemporary life, cyber-net communication technology is nearly ubiquitous, and the information that they are transmitted also almost is oozed
The every aspect of our lives is arrived thoroughly.However, the safety issue of information is increasing, so, people are obtaining letter now
Before breath resource, generally require to verify the identity of oneself, so as to the safety of guarantee information.Because other lifes of face and human body
Thing feature (fingerprint, iris etc.) is equally inherent, and its uniqueness and the superperformance for being difficult to be replicated are that identity differentiates to carry
Necessary premise has been supplied, and user need not specially coordinate face collecting device, almost can be in the state of unconscious just
Facial image can be obtained.
Existing face identification system main operation modes:Identifying system runs on big-and-middle-sized server, and client computer is only held
Row connection server, online image transmitting shows recognition result function.Its workflow mainly includes two stages：
1. gather facial image to upload onto the server, in server data library storage face characteristic；2. when certification is needed, client computer meeting
Image to be certified is uploaded onto the server, face characteristic of the server in data base is contrasted, return the result and arrive
Client computer.
The deficiency of existing face identification system：1. server is bulky, and client functionality is single, and needs networking,
Once suspension, will be unable to realize face verification function；2. system needs the operation of not power-off in 24 hours, and electric quantity consumption is big, and hardware is damaged
Consumption increase, service life reduction；3. image acquisition aspect can not effectively distinguish true man and photo.
The content of the invention
The present invention is in order to solve the above problems, it is proposed that a kind of offline face based on low-power-consumption embedded and infrared triggering
Identifying system and method, the present invention can recognize that whether be that true man are identified, while ensureing the efficient and accuracy of identification.
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of offline face identification system based on low-power-consumption embedded and infrared triggering, including it is image capture module, aobvious
Show module, flush bonding processor, ADC voltage acquisition modules (ADC0809), infrared distance measurement module, UPS+ AC power supply modules and
Common interface module, wherein：
The common interface module, is configured to be connected both-way communication by bus with flush bonding processor, is that image is adopted
Collection module, display module and ADC voltage acquisition modules and flush bonding processor communication provide passage；
The infrared distance measurement module, is configured to be connected with ADC voltage acquisition modules, the distance of detection object, and with electricity
Pressure value returns to ADC voltage acquisition modules；
The ADC voltage acquisition modules, when being configured to have object to enter in identification range according to magnitude of voltage determination, send
Trigger is to flush bonding processor；
The flush bonding processor, is configured to receive trigger, and control image capture module collection image is simultaneously received
The image, determines whether successively face image and mobile, and image and face characteristic will be received when judged result is is
Storage information in storehouse is matched, output matching result；
Described image acquisition module, is configured to receive the control command of flush bonding processor, carries out image acquisition.
The UPS+ AC power supply modules can carry out power input by civilian 200-280v alternating currents, and can be exchanged
It is that embedded system is powered and charged for UPS to direct current conversion, decline can be sent when ups power dump energy is not enough along the pulse
Signal is rushed to flush bonding processor.
Above-mentioned image capture module, ADC voltage acquisition modules, display module, UPS+ AC power supply modules are all by general
Interface module is connected with flush bonding processor.
When the flush bonding processor does not receive trigger, system be in low power consumpting state, image capture module and
Display module does not work.
Be stored with Face datection algorithm, mobile's detection algorithm and face recognition algorithms in the flush bonding processor, enters
Row processed offline.
Based on the face identification method of said system, comprise the following steps：
(1) distance of continuous detection object, and with magnitude of voltage return；
(2) according to the magnitude of voltage for returning, determine whether that object enters identification range, if it did not, return to step (1),
Otherwise, into step (3)；
(3) collection determines whether face image into the subject image of identification range, if it is not, showing mistake letter
Breath, if it is, judge whether current face image belongs to mobile, if it is not, error message is shown, if it is, into step
Suddenly (4)；
(4) face image is matched with the storage information in face characteristic storehouse, output matching result.
In the step (2), when return voltage data are more than pre-set threshold, it is believed that have object to enter identification range.
In the step (3), the image to gathering carries out pretreatment, is filtered, gray proces and normalized make
Be changed into single channel gray-scale maps.
In the step (3), judge whether contain face image in present image, and it is fixed that organ is carried out to face image
Position, to single channel gray-scale maps, is detected using the cascade classifier based on Haar features, judges whether it is face image, with
And the position of face and eyes.
In the step (3), the concrete grammar for determining whether mobile is：According to eyes and the position of face, it is determined that
Its region being located, then carries out principal component analysiss to action during blink and the action of face, continuous finally by analysis
The main constituent attribute of video pictures show that whether the face image is the face image of mobile.
In the step (4), the face image and databases storage face of current active body whether identical side is verified
Method is, using PCA algorithms, will to carry out dimensionality reduction and feature extraction in all face image data spot projections to PCA subspaces, finally
Confidence calculations are carried out using Euclidean distance equation, when confidence level is less than setting confidence level, is judged to identical face.
Certainly, above-mentioned image processing method, face identification method or mobile can be replaced with other existing methods, such as
Image processing method could alternatively be binarization method, and face identification method replaces with Local Features Analysis method, neutral net
Analysis method etc., these belong to the simple replacement that those skilled in the art are readily apparent that, not creative work.
Beneficial effects of the present invention are：
(1) power consumption of the present invention is extremely low, quiet stable, small volume, simple installation, identified off-line, also can be normal without the need for networking
Work, and it is reliable and stable when running；
(2) present invention is portable strong, and execution efficiency is high, and image acquisition, image procossing and interface operation are in multiple lines
Carry out simultaneously in journey, accelerate the execution efficiency of system, take full advantage of the computing capability of CPU；
(3) recognition result of the invention is accurate, recognition speed is fast.
Description of the drawings
Fig. 1 is the system construction drawing of the present invention；
Fig. 2 is the flow chart for creating face image data storehouse of the present invention；
Fig. 3 is the schematic flow sheet of the present invention.
Specific embodiment：
Below in conjunction with the accompanying drawings the invention will be further described with embodiment.
As shown in figure 1, it is a kind of based on low-power-consumption embedded and infrared triggering integrate client computer and server from
Line In vivo detection and face identification system.
The present invention is realized using below scheme：1. on hardware, based on ARM and the embedded system of infrared triggering；2. software
On, can run on the offline In vivo detection and face identification system of PC/ embedded-type ARMs.
Certainly, those skilled in the art are entirely possible under the enlightenment of the present invention, carry out hardware system or software more
Change, these belong to that the simple replacement of creative work need not be paid.
Based on ARM and the main composition of the embedded system of infrared triggering：UPS+ AC power supply modules (0), image acquisition
Module (1), display module (2), the ARM chips (3) with Cortex-A53 as core, ADC voltage acquisition modules (4), infrared survey
Away from module (5), common interface module (6).
With ARM chips (3) by wire both-way communication, image capture module (can select USB to take the photograph common interface module (6)
As head module) (1), display module (can select HDMI touch screens) (2) and ADC voltage acquisition modules (4) are by general-purpose interface
Module (6) connection ARM chips (3) is communicated, and infrared distance measurement module (5) is connected by wire and ADC voltage acquisition modules (4)
Connect, both-way communication, power supply selects UPS+ AC power supply modules (0).
The offline In vivo detection and face identification system that can run on PC/ embedded-type ARMs is a set of software system, and it has
It is characterized in that：1. portable：Write using QT, various running environment can compilation run；2. execution efficiency is high：With many
Thread programming technique gives full play to Cortex-A53 Core Superiorities, and In vivo detection and face recognition algorithms are performed at a high speed；3. operate
Simple and fast, image conversion interface human nature is friendly.
It is provided by the present invention based on low-power-consumption embedded and infrared triggering integrate client computer and server from
The development process of line In vivo detection and face identification system is as follows：Transplant on the ARM chips (3) with Cortex-A53 as core
Uboot and Linux embedded OSs are managed collectively ancillary equipment, and write offline In vivo detection and face knowledge using QT
Cross compile is to the Linux embedded platforms operation transplanted after other system.
It is provided by the present invention based on low-power-consumption embedded and infrared triggering integrate client computer and server from
Line In vivo detection and face identification system realize logic：Offline In vivo detection and face identification system are run on Cortex-
A53 processors are in the ARM chips of core, the state of this system and image capture module (1) is by the tactile of infrared distance measurement module (5)
Signal and determine, when someone is near 1 meter or so of infrared distance measurement and when stopping 2 seconds, system immediately enters working condition (7), first
In vivo detection is carried out, after detection passes through, system can automatically snap your face image, and and be stored in the face characteristic of inside
Matched, display module can output matching information and facial image after being verified；Conversely, then authentication output is unsuccessfully believed
Breath.Whole proof procedure is all completed offline without the need for networking.In one meter or so of infrared distance measurement module, system can be located nobody
In low-power consumption standby state, image capture module (1) and display module (2) are out of service.
The continuous detecting object of above-mentioned infrared distance measurement module (5) meeting and the distance of itself, and ADC is returned in the form of a voltage
Module (4) ADC is communicated by common interface module (4) with the ARM chips (3) with Cortex-A53 as core, offline living
A threshold value is set to infrared distance measurement module (5) returned data in health check-up survey and face identification system, when returned data is more than
This threshold value, system can be activated, in running order (7).ARM chips (3) excellent performance with Cortex-A53 as core,
Be not required in the case of well-ventilated any radiating element just can stablize (SuSE) Linux OS that smooth operation transplant with offline
In vivo detection and face identification system.UPS+ AC power supply modules (0) though use so that the system power-off also can normally make
With can be widely applied to the occasions such as various gate inhibitions, information gathering.Possessing existing face identification system using this design is not had
Power consumption it is extremely low, quiet stable, small volume, simple installation, identified off-line, without the need for networking also can normal work, it is reliable and stable
Advantage.
Offline In vivo detection of the present invention and face identification system run on and low-power-consumption embedded are with Cortex-A53
On the ARM chips (built-in Linux) of core, refer to that its program file and data file is entirely located in offline embedded
In linux system, can read and write without the need for networking, this design has accomplished that server and client are machine integrated, reduces
The complexity that system is installed.Image acquisition is referred to using multithreading, image procossing, interface operation enters simultaneously in multiple threads
OK, the so big execution efficiency for accelerating system, takes full advantage of the computing capability of CPU.The system includes two kinds of Working moulds
Formula, corresponds to respectively corresponding hommization operation interface：1. face database pattern is created, and user can be after input information under the pattern
Face establishment is carried out, face characteristic data correspondence input information is saved in system database；2. Real time identification pattern, the pattern
Lower system can in real time show present image, and when infrared distance measurement module returned data is more than pre-set threshold proof procedure is started.
Offline In vivo detection of the present invention and face identification system comprising image procossing, Face datection, In vivo detection and
Face identification method.
Image procossing refers to and acquired image is filtered that gray proces and normalized are allowed to be changed into single channel
Gray-scale maps, the data volume for making image greatly reduces, and can reduce illumination noise.
Face datection, judges whether contain face in present image, and carries out structures locating to face；To previous step
The single channel gray-scale maps of (image procossing) are utilized and detected based on the cascade classifier of Haar features, judge whether it is face,
And the position of face and eyes.
In vivo detection, judges whether current face is living person rather than photo；The method is one kind based on eyes and face
Motion mode feature is recognizing living body faces.First, the region at its place is drawn according to the position of eyes and face, then to blinking
Action and the action of face at the moment carries out PCA (principal component analysiss), finally by a series of main constituent for analyzing video pictures
Attribute show whether the face is living body faces.
Recognition of face, verifies whether current living body faces are identical with databases storage face；Recognizer is calculated using PCA
Method, face images data point is projected to and carry out in PCA subspaces dimensionality reduction and feature extraction.Finally utilize Euclidean distance equation
Confidence calculations are carried out, when confidence level is less than setting confidence level, is judged to identical face.
As shown in figure 1, carrying out face database establishment by the establishment face database pattern of system, local data base is saved it in
In.
As shown in Fig. 2 face database is created after finishing, system enters Real time identification pattern and is in low-power consumption standby pattern,
When user near when, infrared distance measurement module returned data is more than given threshold, and system is activated, into mode of operation, adopts automatically
Collection image, and image procossing, In vivo detection, Face datection and recognition of face are carried out to it, finally result is shown to into display
In module.
Although the above-mentioned accompanying drawing that combines is described to the specific embodiment of the present invention, not to present invention protection model
The restriction enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need the various modifications made by paying creative work or deformation still within protection scope of the present invention.
Claims (10)
1. a kind of offline face identification system based on low-power-consumption embedded and infrared triggering, is characterized in that：Including image acquisition
Module, display module, flush bonding processor, ADC voltage acquisition modules, infrared distance measurement module and interface module, wherein：
The interface module, is configured to flush bonding processor by wire both-way communication, is image capture module, shows mould
Block provides passage with ADC voltage acquisition modules and flush bonding processor communication；
The infrared distance measurement module, is configured to be connected with ADC voltage acquisition modules, the distance of detection object, and with magnitude of voltage
Return to ADC voltage acquisition modules；
The ADC voltage acquisition modules, when being configured to have object to enter in identification range according to magnitude of voltage determination, send triggering
Signal is to flush bonding processor；
The flush bonding processor, is configured to receive trigger, and control image capture module collection image simultaneously receives the figure
Picture, determines whether successively face image and mobile, will receive in image and face characteristic storehouse when judged result is is
Storage information matched, output matching result；
Described image acquisition module, is configured to receive the control command of flush bonding processor, carries out image acquisition.
2. a kind of offline face identification system based on low-power-consumption embedded and infrared triggering as claimed in claim 1, its spy
Levying is：Also power input can be carried out by civilian 200-280v alternating currents including UPS+ AC power supply modules, and can be exchanged
It is that embedded system is powered and charged for UPS to direct current conversion, decline can be sent when ups power dump energy is not enough along the pulse
Signal is rushed to flush bonding processor.
3. a kind of offline face identification system based on low-power-consumption embedded and infrared triggering as claimed in claim 1, its spy
Levying is：When the flush bonding processor does not receive trigger, system is in low power consumpting state, image capture module and display
Module does not work.
4. a kind of offline face identification system based on low-power-consumption embedded and infrared triggering as claimed in claim 1, its spy
Levying is：Be stored with Face datection algorithm, mobile's detection algorithm and face recognition algorithms in the flush bonding processor, carry out from
Line process.
5. the face identification method based on the system as any one of claim 1-4, is characterized in that：Including following step
Suddenly：
(1) distance of continuous detection object, and with magnitude of voltage return；
(2) according to the magnitude of voltage for returning, determine whether that object enters identification range, if it did not, return to step (1), otherwise,
Into step (3)；
(3) collection determines whether face image into the subject image of identification range, if it is not, error message is shown, such as
Fruit is judge whether current face image belongs to mobile, if it is not, display error message, if it is, into step (4)；
(4) face image is matched with the storage information in face characteristic storehouse, output matching result.
6. face identification method as claimed in claim 5, is characterized in that：In the step (2), return voltage data are more than pre-
During given threshold, it is believed that have object to enter identification range.
7. face identification method as claimed in claim 5, is characterized in that：In the step (3), the image to gathering carries out pre-
Process, be filtered, gray proces and normalized are allowed to be changed into single channel gray-scale maps.
8. face identification method as claimed in claim 5, is characterized in that：In the step (3), whether judge in present image
Containing face image, and structures locating is carried out to face image, to single channel gray-scale maps, using the cascade point based on Haar features
Class device detected, judges whether it is face image, and the position of face and eyes.
9. face identification method as claimed in claim 5, is characterized in that：In the step (3), mobile is determined whether
Concrete grammar is：According to eyes and the position of face, its region being located is determined, then to the dynamic of action during blink and face
Principal component analysiss are carried out, show whether the face image is living finally by the main constituent attribute for analyzing continuous video pictures
The face image of kinetoplast.
10. face identification method as claimed in claim 5, is characterized in that：In the step (4), current active body is verified
Whether identical method is, using PCA algorithms, all face image data points to be thrown to face image with databases storage face
Shadow carries out dimensionality reduction and feature extraction in PCA subspaces, finally carries out confidence calculations using Euclidean distance equation, works as confidence level
During less than setting confidence level, it is judged to identical face.
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