CN108492421A - Low-power consumption face identification method - Google Patents
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Abstract
In order to improve the recognition accuracy to real human body characteristic parameter, improve the identification safety of gate inhibition's unit, the present invention provides a kind of low-power consumption face identification methods, based on gate inhibition's unit with face acquisition device, including local, long-range two parts are identified respectively and comprehensive descision, the prompt of None- identified is provided for the false medium being identified, the prompt of identification success or failure is provided for the true medium that can be identified, the influence of the drift generated in temperature-rise period to gate inhibition's unit identification sensor of itself by way of heating in identification process, which is not given, to be eliminated, but it energetically pays attention to and has carried out the comparison that square root calculates this experience calculation in the calculating of testing result twice, to be omitted in analysis and calculating, square root when calculating v is eliminated in calculating.Through experiment, 40% or so is improved for the recognition accuracy of false body to be identified.
Description
Technical field
The invention belongs to security protections and technical field of image processing, and in particular to a kind of low-power consumption face identification method.
Background technology
Living things feature recognition is a kind of new identity identifying technology.In actual life, everyone has with other people not
Same unique biological characteristic.With the development of computer technology, people can extract the biological information of itself, than
Such as face, face refer to vein, iris, vocal print.The technology that this physical trait by people carries out identification is referred to as giving birth to
Object feature identification technique.
Face recognition technology is a kind of biometrics identification technology, with the advantages such as convenient, fast, accurate, in recent years
Obtain the development advanced by leaps and bounds.The input terminal input of face identification system is usually a face for containing identity to be detected
The facial image of several known identities in image and face database, and its output is then that a series of human face similarity degrees obtain
Point, with the identity of this face for showing identification.At present, face recognition technology is widely used to criminal investigation and case detection, department of banking
The fields such as system, customs inspection, the civil affairs department, work and rest attendance.However, with the continuous extension of face recognition technology application range,
Some safety problems also occur therewith, and criminal cheats face identification system using the human face photo forged, to legal
User causes heavy economic losses.Therefore, the judgement of the source authenticity of facial image is particularly important, here it is live bodies
Detection.
Invention content
In view of the above analysis knowledge of gate inhibition's unit is improved in order to improve the recognition accuracy to real human body characteristic parameter
Other safety, the present invention provides a kind of low-power consumption face identification methods, based on gate inhibition's unit with face acquisition device, packet
It includes：
(1) the first facial image is obtained by gate inhibition's unit, first facial image includes first part and second
Point, the data volume of wherein first part is the at most one third of the data volume of second part；
(2) the first local analytics are carried out to the first part of the first facial image, obtains the first local analysis result；
(3) the first remote analysis is carried out to the second part of the first facial image, obtains the first remote analysis result；
(4) when the first local analysis result, which meets, is less than predetermined the First Eigenvalue, pass through gate inhibition's unit
Again face is detected after heating obtains the second facial image, while detecting the identification plane of finger to be identified and identification sensor
Polar coordinates angle thetamn∈ [0,1], second facial image include Part III and Part IV, the wherein data of Part III
Amount is at most the 30% of the data volume of Part IV；Otherwise prompt None- identified is without obtaining the second facial image；
(5) the first local analytics are carried out to the Part III of the second facial image, obtains the second local analytics result；
(6) the second remote analysis is carried out to the Part IV of the second facial image, obtains the second remote analysis result；
According to the first local analysis result, the second local analytics result, the first remote analysis result and the second remote analysis as a result, determining
The face recognition result of gate inhibition's unit.
Further, the described first local analysis bag includes：
(1A) is using centered on the image geometry center of first part, preset length is data in the neighborhood of radius as waiting locating
First part's image data of reason；
(1B) carries out binaryzation and noise reduction process to pending first part's image data, obtains data set O；
The data set O is carried out symmetry encryption by (1C), obtains data set O '；
(1D) data set O ' and preset reference human face data M are handled as follows：Preset reference human face data M is carried out
Binary conversion treatment obtains M '；The diagonal matrix of data set O ' is calculated；According to the exponent number of the diagonal matrix, from the data set M '
First, upper left corner value starts to intercept the intermediary matrix P of same exponent number；Calculate the square that intermediary matrix P is obtained with data set O ' multiplication crosses
The characteristic value K1 of battle array.
Further, first remote analysis includes：
(2A) is transferred to remote server to the second part of first facial image；
(2B) carries out Hash operations to the data of the second part received in remote server and obtains and the second part
Corresponding first hash value；
(2C) matrix data K corresponding to second part matrix Ss corresponding with preset reference human face data M is multiplied,
One that wherein exponent number is smaller in the two matrixes obtains matrix K with diagonal matrix polishing after multiplication '；
(2D) is centered on the gray scale barycenter of the image of the second part, the preset length is radius, obtains second
Partial image data matrix L projects matrix L to obtain matrix L ', by matrix L ' and matrix K ' carry out multiplication cross, wherein this
Terminate smaller one in two matrixes with diagonal matrix polishing, matrix Q is obtained after multiplication cross；
The characteristic value F of (2E) calculating matrix Q.
Further, second local analytics include：
(3A) is using centered on the image geometry center of Part III, preset length is data in the neighborhood of radius as waiting locating
The Part III image data of reason；
(3B) carries out binaryzation and noise reduction process to pending Part III image data, obtains data set R；
The data set R is carried out symmetry encryption by (3C) by key of the average value of data set O, obtains data set R '；
(3D) data set R ' and preset reference human face data M are handled as follows：Preset reference human face data M is carried out
Binary conversion treatment obtains M '；The diagonal matrix of data set R ' is calculated；According to the exponent number of the diagonal matrix, from the data set M '
First, upper left corner value starts to intercept the intermediary matrix T of same exponent number；Calculate the square that intermediary matrix T is obtained with data set R ' multiplication crosses
The characteristic value K2 of battle array.
Further, second remote analysis includes：
(4A) is transferred to remote server to the Part IV of second facial image；
(4B) is set
Wherein, kmnIndicate the gray value of the image pixel (m, n) of Part IV；
Greyscale transformation Tr () is carried out to the image of Part IV and obtains θ 'mn：
N is the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is determined by face Boundary Recognition empirical value, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Calculate the characteristic value E of the image boundary matrix；
(4C) carries out Hash operations to the data of the Part IV received in remote server and obtains and the Part IV
Corresponding second hash value；
(4D) carries out Hash operations to preset reference human face data and obtains third hash value.
Further, it is determined that the face recognition result of gate inhibition's unit includes：
First hash value is added with the second hash value and carries out consistency Hash operations by (5A), by operation result and third
Hash value calculates degree a similar to each other using MinHash algorithms；
(5B) is determinedWhether be less than preset recognition threshold, when less than when carry
Show and identify successfully, otherwise prompts recognition failures.
Technical scheme of the present invention has the following advantages：
The low-power consumption face identification method of the present invention can be based on long-range and factor calculating, identification are locally identified respectively
The influence of the drift generated in temperature-rise period to gate inhibition's unit identification sensor of itself by way of heating in the process is simultaneously
It does not give and eliminates, but energetically pay attention to and carried out square root in the calculating of testing result twice and calculate this experience
The comparison of calculation, to be omitted (square root when calculating v is eliminated in calculating) warp in analysis and calculating
Experiment improves 40% or so for the recognition accuracy of false body to be identified.
Specific implementation mode
Low-power consumption face identification method according to a preferred embodiment of the invention, based on the gate inhibition with face acquisition device
Unit, including：
(1) the first facial image is obtained by gate inhibition's unit, first facial image includes first part and second
Point, the data volume of wherein first part is the at most one third of the data volume of second part；
(2) the first local analytics are carried out to the first part of the first facial image, obtains the first local analysis result；
(3) the first remote analysis is carried out to the second part of the first facial image, obtains the first remote analysis result；
(4) when the first local analysis result, which meets, is less than predetermined the First Eigenvalue, pass through gate inhibition's unit
Again face is detected after heating obtains the second facial image, while detecting the identification plane of finger to be identified and identification sensor
Polar coordinates angle thetamn∈ [0,1], second facial image include Part III and Part IV, the wherein data of Part III
Amount is at most the 30% of the data volume of Part IV；Otherwise prompt None- identified is without obtaining the second facial image；
(5) the first local analytics are carried out to the Part III of the second facial image, obtains the second local analytics result；
(6) the second remote analysis is carried out to the Part IV of the second facial image, obtains the second remote analysis result；
According to the first local analysis result, the second local analytics result, the first remote analysis result and the second remote analysis as a result, determining
The face recognition result of gate inhibition's unit.
Preferably, the described first local analysis bag includes：
(1A) is using centered on the image geometry center of first part, preset length is data in the neighborhood of radius as waiting locating
First part's image data of reason；
(1B) carries out binaryzation and noise reduction process to pending first part's image data, obtains data set O；
The data set O is carried out symmetry encryption by (1C), obtains data set O '；
(1D) data set O ' and preset reference human face data M are handled as follows：Preset reference human face data M is carried out
Binary conversion treatment obtains M '；The diagonal matrix of data set O ' is calculated；According to the exponent number of the diagonal matrix, from the data set M '
First, upper left corner value starts to intercept the intermediary matrix P of same exponent number；Calculate the square that intermediary matrix P is obtained with data set O ' multiplication crosses
The characteristic value K1 of battle array.
Preferably, first remote analysis includes：
(2A) is transferred to remote server to the second part of first facial image；
(2B) carries out Hash operations to the data of the second part received in remote server and obtains and the second part
Corresponding first hash value；
(2C) matrix data K corresponding to second part matrix Ss corresponding with preset reference human face data M is multiplied,
One that wherein exponent number is smaller in the two matrixes obtains matrix K with diagonal matrix polishing after multiplication '；
(2D) is centered on the gray scale barycenter of the image of the second part, the preset length is radius, obtains second
Partial image data matrix L projects matrix L to obtain matrix L ', by matrix L ' and matrix K ' carry out multiplication cross, wherein this
Terminate smaller one in two matrixes with diagonal matrix polishing, matrix Q is obtained after multiplication cross；
The characteristic value F of (2E) calculating matrix Q.
Preferably, second local analytics include：
(3A) is using centered on the image geometry center of Part III, preset length is data in the neighborhood of radius as waiting locating
The Part III image data of reason；
(3B) carries out binaryzation and noise reduction process to pending Part III image data, obtains data set R；
The data set R is carried out symmetry encryption by (3C) by key of the average value of data set O, obtains data set R '；
(3D) data set R ' and preset reference human face data M are handled as follows：Preset reference human face data M is carried out
Binary conversion treatment obtains M '；The diagonal matrix of data set R ' is calculated；According to the exponent number of the diagonal matrix, from the data set M '
First, upper left corner value starts to intercept the intermediary matrix T of same exponent number；Calculate the square that intermediary matrix T is obtained with data set R ' multiplication crosses
The characteristic value K2 of battle array.
Preferably, second remote analysis includes：
(4A) is transferred to remote server to the Part IV of second facial image；
(4B) is set
Wherein, kmnIndicate the gray value of the image pixel (m, n) of Part IV；
Greyscale transformation Tr () is carried out to the image of Part IV and obtains θ 'mn：
N is the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is determined by face Boundary Recognition empirical value, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Calculate the characteristic value E of the image boundary matrix；
(4C) carries out Hash operations to the data of the Part IV received in remote server and obtains and the Part IV
Corresponding second hash value；
(4D) carries out Hash operations to preset reference human face data and obtains third hash value.
Preferably, determine that the face recognition result of gate inhibition's unit includes：
First hash value is added with the second hash value and carries out consistency Hash operations by (5A), by operation result and third
Hash value calculates degree a similar to each other using MinHash algorithms；
(5B) is determinedWhether be less than preset recognition threshold, when less than when carry
Show and identify successfully, otherwise prompts recognition failures.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (6)
1. a kind of low-power consumption face identification method, based on gate inhibition's unit with face acquisition device, including：
(1) the first facial image being obtained by gate inhibition's unit, first facial image includes first part and second part,
The data volume of middle first part is the at most one third of the data volume of second part；
(2) the first local analytics are carried out to the first part of the first facial image, obtains the first local analysis result；
(3) the first remote analysis is carried out to the second part of the first facial image, obtains the first remote analysis result；
(4) it when the first local analysis result, which meets, is less than predetermined the First Eigenvalue, is heated up by gate inhibition's unit
Detection face obtains the second facial image again afterwards, while the pole for detecting the identification plane of finger to be identified and identification sensor is sat
Mark angle thetamn∈ [0,1], second facial image include Part III and Part IV, and the data volume of wherein Part III is
At most the 30% of the data volume of Part IV；Otherwise prompt None- identified is without obtaining the second facial image；
(5) the first local analytics are carried out to the Part III of the second facial image, obtains the second local analytics result；
(6) the second remote analysis is carried out to the Part IV of the second facial image, obtains the second remote analysis result；According to
First local analysis result, the second local analytics result, the first remote analysis result and the second remote analysis are as a result, described in determining
The face recognition result of gate inhibition's unit.
2. according to the method described in claim 1, it is characterized in that, the described first local analysis bag includes：
(1A) is using centered on the image geometry center of first part, preset length is data in the neighborhood of radius as pending
First part's image data；
(1B) carries out binaryzation and noise reduction process to pending first part's image data, obtains data set O；
The data set O is carried out symmetry encryption by (1C), obtains data set O '；
(1D) data set O ' and preset reference human face data M are handled as follows：Two-value is carried out to preset reference human face data M
Change handles to obtain M '；The diagonal matrix of data set O ' is calculated；According to the exponent number of the diagonal matrix, from the upper lefts the data set M '
First, angle value starts to intercept the intermediary matrix P of same exponent number；Calculate the matrix that intermediary matrix P is obtained with data set O ' multiplication crosses
Characteristic value K1.
3. according to the method described in claim 2, it is characterized in that, first remote analysis includes：
(2A) is transferred to remote server to the second part of first facial image；
(2B) obtains the data progress Hash operations of the second part received in remote server opposite with the second part
The first hash value answered；
(2C) matrix data K corresponding to second part matrix Ss corresponding with preset reference human face data M is multiplied, wherein
Exponent number is smaller in the two matrixes one obtains matrix K with diagonal matrix polishing after multiplication '；
(2D) is centered on the gray scale barycenter of the image of the second part, the preset length is radius, obtains second part
Image data matrix L, projected to obtain matrix L ', by matrix L ' and matrix K to matrix L ' carry out multiplication cross, wherein the two
Terminate smaller one in matrix with diagonal matrix polishing, matrix Q is obtained after multiplication cross；
The characteristic value F of (2E) calculating matrix Q.
4. according to the method described in claim 3, it is characterized in that, second local analytics include：
(3A) is using centered on the image geometry center of Part III, preset length is data in the neighborhood of radius as pending
Part III image data；
(3B) carries out binaryzation and noise reduction process to pending Part III image data, obtains data set R；
The data set R is carried out symmetry encryption by (3C) by key of the average value of data set O, obtains data set R '；
(3D) data set R ' and preset reference human face data M are handled as follows：Two-value is carried out to preset reference human face data M
Change handles to obtain M '；The diagonal matrix of data set R ' is calculated；According to the exponent number of the diagonal matrix, from the upper lefts the data set M '
First, angle value starts to intercept the intermediary matrix T of same exponent number；Calculate the matrix that intermediary matrix T is obtained with data set R ' multiplication crosses
Characteristic value K2.
5. according to the method described in claim 4, it is characterized in that, second remote analysis includes：
(4A) is transferred to remote server to the Part IV of second facial image；
(4B) is set
Wherein, kmnIndicate the gray value of the image pixel (m, n) of Part IV；
Greyscale transformation Tr () is carried out to the image of Part IV and obtains θ 'mn：
N is the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is determined by face Boundary Recognition empirical value, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Calculate the characteristic value E of the image boundary matrix；
(4C) obtains the data progress Hash operations of the Part IV received in remote server opposite with the Part IV
The second hash value answered；
(4D) carries out Hash operations to preset reference human face data and obtains third hash value.
6. according to the method described in claim 5, it is characterized in that, determining that the face recognition result of gate inhibition's unit includes：
First hash value is added with the second hash value and is carried out consistency Hash operations by (5A), by operation result and the 3rd Hash
Value calculates degree a similar to each other using MinHash algorithms；
(5B) is determinedWhether be less than preset recognition threshold, when less than when prompt know
Not Cheng Gong, otherwise prompt recognition failures.
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Application publication date: 20180904 |