CN108446639A - Low-power consumption augmented reality equipment - Google Patents
Low-power consumption augmented reality equipment Download PDFInfo
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
In order to reduce the complexity of recognition of face, the present invention provides a kind of low-power consumption augmented reality equipment.The present invention can accurately realize the processing of facial recognition data and the feature extraction of face texture information, meanwhile, the relatively easy easy realization of algorithm can realize the distributed AR data transmissions based on recognition of face of low-power consumption.
Description
Technical field
The invention belongs to technical field of face recognition, and in particular to a kind of low-power consumption augmented reality equipment.
Background technology
The computer technology compared using analysis is refered in particular in recognition of face.Recognition of face is a popular computer technology
Research field, face tracking detecting, adjust automatically image zoom, night infrared detecting, adjust automatically exposure intensity；It belongs to raw
Object feature identification technique is to distinguish organism individual to organism (generally the refering in particular to people) biological characteristic of itself.
Face recognition technology is the face feature based on people, to the facial image or video flowing of input.First determine whether it
With the presence or absence of face, if there is face, then the position of each face, size and each major facial organ are further provided
Location information.And according to these information, the identity characteristic contained in each face is further extracted, and by itself and known people
Face is compared, to identify the identity of each face.
Current low-power consumption augmented reality equipment includes very much, but all have the shortcomings that it is respective, below we analyze one by one：
(101) face identification method of geometric properties, geometric properties generally refer to eye, nose, mouth etc. shape and they it
Between mutual geometrical relationship the distance between such as, it is fast using this algorithm recognition speed, but discrimination is relatively low.
(102) face identification method of feature based face (PCA):Eigenface method is the recognition of face side converted based on KL
Method, KL transformation are a kind of optimal orthogonal transformations of compression of images.The image space of higher-dimension obtained after KL is converted one group it is new
Orthogonal basis retains wherein important orthogonal basis, low-dimensional linear space can be turned by these bases.If it is assumed that face is low at these
The projection in dimensional linear space has separability, so that it may with by these characteristic vectors of the projection as identification, here it is eigenface sides
The basic thought of method.These methods need more training sample, and be based entirely on the statistical property of gradation of image.
(3) face identification method of neural network:The input of neural network can be the facial image for reducing resolution ratio, office
The auto-correlation function in portion region, second moment of local grain etc..Such methods also need more sample and are trained, and
In many applications, sample size is very limited.
(4) face identification method of elastic graph matching:Elastic graph matching method defined in the two-dimensional space it is a kind of for
Common Facial metamorphosis has the distance of certain invariance, and represents face, any of topological diagram using attribute topological diagram
Vertex includes a feature vector, for recording information of the face near the vertex position.This method combines gamma characteristic
And geometrical factor, can allow image there are elastic deformation when comparing, overcome expression shape change to the influence of identification in terms of receive
Preferable effect has been arrived, also multiple samples has no longer been needed to be trained simultaneously for single people, but algorithm is relative complex.
(105) face identification method of support vector machines (SVM):Support vector machines is one of statistical-simulation spectrometry field
New hot spot, it attempts so that learning machine reaches a kind of compromise on empiric risk and generalization ability, to improve learning machine
Performance.What support vector machines mainly solved is 2 classification problems, its basic thought be attempt to low-dimensional it is linear not
The problem of can dividing, is converted to the problem of linear separability of a higher-dimension.It is common the experimental results showed that SVM has preferable discrimination,
It require that a large amount of training sample (per class 300), this is often unpractical in practical applications.And supporting vector
The machine training time is long, and method realizes that complexity, the function follow the example of ununified theory.
Invention content
In view of the above analysis, the main purpose of the present invention is to provide a kind of low-power consumption augmented reality equipment, including：
Face recognition data obtaining unit, for carrying out positioned at the distributed user images face of predesignated subscriber's surroundings
Portion identify, when the image information in recognition result meets preset condition, obtain predesignated subscriber's surroundings dynamic and/
Or static image information is used as distribution AR data；
Preset time acquiring unit, for obtaining the first preset time and the second preset time；
Low power consumption transmission unit, for when the transmission per unit of time data volume of distributed AR data is less than preset unit
Between transmitted data amount when, to be acquired with relevant first predetermined manner of the first preset time and transmit distributed AR data, otherwise
To be acquired with relevant second predetermined manner of the second preset time and transmit distributed AR data.
Further, first preset time is more than the second preset time, and first predetermined manner is sampling frequency
Rate is higher than the sample mode of the sample frequency under the second predetermined manner.
Further, the low-power consumption AR transmission units include：
Human face data acquires subelement, and the face image data for reading people is taken the photograph using the multiple of multiple angles first
As head absorbs the facial image that band has powerful connections；
Face recognition subelement passes through confirmation for being detected to face from the complex background image of above-mentioned intake
The face image of the face character extraction people of detected object；
The face image of wherein extraction people includes that its boundary is calculated and identified comprising following calculating process：
Wherein, kmnIndicate the gray value of image pixel (m, n), K=max (kmn), shooting angle θmn∈[0,1]
Radian greyscale transformation is carried out to image using Tr formula：
N is the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is tested and determined by face Boundary Recognition, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Then image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Wherein
k′mn=| k 'mn-min{k′ij}|,(i,j)∈W
W is 3 × 3 windows centered on pixel (i, j),
Then boundary judging result is verified, if identification enough, terminates, if being not enough to identify, to upper
It states Boundary Recognition threshold value to be adjusted, repeat the above process, until obtaining good Boundary Recognition result, wherein Boundary Recognition threshold
It is [0.3,0.8] to be worth value range；
First judgment sub-unit judges that the factor includes face's posture, illumination for being judged for the first time identification image
It spends, have unobstructed, face's distance, be to carry out face's posture judgement first, symmetry is carried out to identification image and integrity degree judges,
The symmetry of the image of above-mentioned second step acquisition is analyzed, if symmetry meets predetermined threshold value requirement, then it is assumed that face
Flat-hand position is correct, if it exceeds predetermined threshold value require, then it is assumed that face's flat-hand position is incorrect, that is, occur side face excessively or face
Portion's overbank phenomenon, specific to judge algorithm to carry out binaryzation to obtained image, it is 80 to take threshold value, is more than 80 pixel
0 is taken, remaining sets 1, is divided into the projection that left and right two parts seek horizontal direction respectively to the image after binaryzation, obtains two-part
Histogram calculates the chi-Square measure between histogram, and it is horizontal poorer to symmetry that chi-Square measure shows more greatly, then complete to face
Whole degree judged, i.e., to face's finite element inspection in the face mask that identifies, check its eye, eyebrow, face, under
Bar whether occur completely, if lacking some element or imperfect, then it is assumed that pitch angle is excessive when identification, then has to face
It is unobstructed to be judged, subsequent processing is carried out when unobstructed, finally whether face's distance is properly judged, when suitable identification
Apart from when, carry out subsequent processing, when the conditions are satisfied, carry out below step.
Second judgment sub-unit, the position for searching for crucial human face characteristic point in the specific region of face image
It sets, the grey level histogram using human eye candidate region in identification image is divided, the part that carrying out image threshold segmentation takes gray value minimum
The value of pixel is 255, and the value of other pixels is 0, and second judgment sub-unit further includes pupil center's locator unit,
For detecting pip from two eye areas, the detection of eyes block is carried out using position and luminance information, from left and right eye
The higher connection block of brightness is deleted in region in the image of binaryzation, select the connection block for being located at extreme lower position as eyes block,
And above-mentioned Pupil diameter subelement further includes that low-power consumption processing subelement retains brightness point for carrying out chroma space
Amount, obtains the luminance picture of human eye area, enhances into column hisgram linear equalization and contrast luminance picture, then carries out threshold
Value transformation carries out corrosion and expansion process to the image after threshold transformation, then implements to treated two-value human eye area
Gauss is filtered with median smoothing, carries out threshold transformation again to the image after smooth, then carry out edge detection, ellipse fitting is simultaneously examined
Circle in measuring wheel exterior feature, the maximum circle of detection radius obtain the center of pupil；
Texture feature information obtains subelement and is handled facial recognition data after carrying out above-mentioned positioning, uses
High-pass filter, the Gaussian function that graphics standard is melted into a zero-mean and unit variance is distributed, then carries out sub-block to image
Segmentation, dimension-reduction treatment calculate the two-value relationship of the gray value on each pixel value of image point adjacent thereto and secondly pass through correspondence
Pixel value point and weighting multiplied by weight, are then added the coding for foring local binary patterns, finally by using multizone
Textural characteristics of the histogram as image, Local textural feature calculation formula are as follows：
Hi,j=∑x,yI { h (x, y)=i } I { (x, y) ∈ Rj), i=0,1 ... n-1；J=0,1 ... D-1
Wherein Hi,jIndicate the region R divided from imagejIn belong to the number of i-th of histogram, n is local binary
The number of the statistical model feature of pattern, D is the areal of facial image, to the upper of face key area and non-key area
It states information to be counted, then be spliced, synthesis obtains the texture feature information of whole picture face image；
Compare and transmission subelement, for the texture feature information and face shelves to whole picture face image obtained above
Face texture characteristic information in case database is compared and is transmitted, to realize that the enhancing based on low-power consumption recognition of face is existing
Real data obtains and transmission.
Technical scheme of the present invention has the following advantages：
It can accurately realize the processing of facial recognition data and the feature extraction of face texture information, meanwhile, it calculates
The relatively easy easy realization of method, can realize based on recognition of face and identify crowd's recognition of face of condition, and then reduces and divide
Power consumption needed for cloth AR data transmissions but ensure effective AR data resolutions.
Description of the drawings
Fig. 1 shows augmented reality equipment composition frame chart according to the present invention.
Specific implementation mode
As shown in Figure 1, the low-power consumption augmented reality equipment of the present invention includes：
Face recognition data obtaining unit, for carrying out positioned at the distributed user images face of predesignated subscriber's surroundings
Portion identify, when the image information in recognition result meets preset condition, obtain predesignated subscriber's surroundings dynamic and/
Or static image information is used as distribution AR data；
Preset time acquiring unit, for obtaining the first preset time and the second preset time；
Low power consumption transmission unit, for when the transmission per unit of time data volume of distributed AR data is less than preset unit
Between transmitted data amount when, to be acquired with relevant first predetermined manner of the first preset time and transmit distributed AR data, otherwise
To be acquired with relevant second predetermined manner of the second preset time and transmit distributed AR data.
Preferably, first preset time is more than the second preset time, and first predetermined manner is sample frequency
Higher than the sample mode of the sample frequency under the second predetermined manner.
Preferably, the low-power consumption AR transmission units include：
Human face data acquires subelement, and the face image data for reading people is taken the photograph using the multiple of multiple angles first
As head absorbs the facial image that band has powerful connections；
Face recognition subelement passes through confirmation for being detected to face from the complex background image of above-mentioned intake
The face image of the face character extraction people of detected object；
The face image of wherein extraction people includes that its boundary is calculated and identified comprising following calculating process：
Wherein, kmnIndicate the gray value of image pixel (m, n), K=max (kmn), shooting angle θmn∈[0,1]
Radian greyscale transformation is carried out to image using Tr formula：
N is the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is tested and determined by face Boundary Recognition, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Then image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Wherein
k′mn=| k 'mn-min{k′ij},(i,j)∈W
W is 3 × 3 windows centered on pixel (i, j),
Then boundary judging result is verified, if identification enough, terminates, if being not enough to identify, to upper
It states Boundary Recognition threshold value to be adjusted, repeat the above process, until obtaining good Boundary Recognition result, wherein Boundary Recognition threshold
It is [0.3,0.8] to be worth value range；
First judgment sub-unit judges that the factor includes face's posture, illumination for being judged for the first time identification image
It spends, have unobstructed, face's distance, be to carry out face's posture judgement first, symmetry is carried out to identification image and integrity degree judges,
The symmetry of the image of above-mentioned second step acquisition is analyzed, if symmetry meets predetermined threshold value requirement, then it is assumed that face
Flat-hand position is correct, if it exceeds predetermined threshold value require, then it is assumed that face's flat-hand position is incorrect, that is, occur side face excessively or face
Portion's overbank phenomenon, specific to judge algorithm to carry out binaryzation to obtained image, it is 80 to take threshold value, is more than 80 pixel
0 is taken, remaining sets 1, is divided into the projection that left and right two parts seek horizontal direction respectively to the image after binaryzation, obtains two-part
Histogram calculates the chi-Square measure between histogram, and it is horizontal poorer to symmetry that chi-Square measure shows more greatly, then complete to face
Whole degree judged, i.e., to face's finite element inspection in the face mask that identifies, check its eye, eyebrow, face, under
Bar whether occur completely, if lacking some element or imperfect, then it is assumed that pitch angle is excessive when identification, then has to face
It is unobstructed to be judged, subsequent processing is carried out when unobstructed, finally whether face's distance is properly judged, when suitable identification
Apart from when, carry out subsequent processing, when the conditions are satisfied, carry out below step.
Second judgment sub-unit, the position for searching for crucial human face characteristic point in the specific region of face image
It sets, the grey level histogram using human eye candidate region in identification image is divided, the part that carrying out image threshold segmentation takes gray value minimum
The value of pixel is 255, and the value of other pixels is 0, and second judgment sub-unit further includes pupil center's locator unit,
For detecting pip from two eye areas, the detection of eyes block is carried out using position and luminance information, from left and right eye
The higher connection block of brightness is deleted in region in the image of binaryzation, select the connection block for being located at extreme lower position as eyes block,
And above-mentioned Pupil diameter subelement further includes that low-power consumption processing subelement retains brightness point for carrying out chroma space
Amount, obtains the luminance picture of human eye area, enhances into column hisgram linear equalization and contrast luminance picture, then carries out threshold
Value transformation carries out corrosion and expansion process to the image after threshold transformation, then implements to treated two-value human eye area
Gauss is filtered with median smoothing, carries out threshold transformation again to the image after smooth, then carry out edge detection, ellipse fitting is simultaneously examined
Circle in measuring wheel exterior feature, the maximum circle of detection radius obtain the center of pupil；
Texture feature information obtains subelement and is handled facial recognition data after carrying out above-mentioned positioning, uses
High-pass filter, the Gaussian function that graphics standard is melted into a zero-mean and unit variance is distributed, then carries out sub-block to image
Segmentation, dimension-reduction treatment calculate the two-value relationship of the gray value on each pixel value of image point adjacent thereto and secondly pass through correspondence
Pixel value point and weighting multiplied by weight, are then added the coding for foring local binary patterns, finally by using multizone
Textural characteristics of the histogram as image, Local textural feature calculation formula are as follows：
Hi,j=∑x,yI { h (x, y)=i } I { (x, y) ∈ Rj), i=0,1 ... n-1；J=0,1 ... D-1
Wherein Hi,jIndicate the region R divided from imagejIn belong to the number of i-th of histogram, n is local binary
The number of the statistical model feature of pattern, D is the areal of facial image, to the upper of face key area and non-key area
It states information to be counted, then be spliced, synthesis obtains the texture feature information of whole picture face image；
Compare and transmission subelement, for the texture feature information and face shelves to whole picture face image obtained above
Face texture characteristic information in case database is compared and is transmitted, to realize that the enhancing based on low-power consumption recognition of face is existing
Real data obtains and transmission.
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 (3)
1. a kind of low-power consumption augmented reality equipment, which is characterized in that including：
Face recognition data obtaining unit, for carrying out the distributed user images face knowledge positioned at predesignated subscriber's surroundings
Not, when the image information in recognition result meets preset condition, the dynamic of predesignated subscriber's surroundings and/or quiet is obtained
State image information is used as distribution AR data；
Preset time acquiring unit, for obtaining the first preset time and the second preset time；
Low power consumption transmission unit is passed for the transmission per unit of time data volume when distribution AR data less than the preset unit interval
When transmission of data amount, to be acquired with relevant first predetermined manner of the first preset time and transmit distributed AR data, otherwise with
Relevant second predetermined manner of second preset time acquires and transmits distributed AR data.
2. according to the method described in claim 1, it is characterized in that, first preset time be more than the second preset time, and
First predetermined manner is sample mode of the sample frequency higher than the sample frequency under the second predetermined manner.
3. according to the method described in claim 2, it is characterized in that, the low-power consumption AR transmission units include：
Human face data acquires subelement, and the face image data for reading people uses multiple cameras of multiple angles first
The facial image intake having powerful connections to band；
Face recognition subelement, it is tested by confirming from the complex background image of above-mentioned intake for being detected to face
Survey the face image of the face character extraction people of object；
The face image of wherein extraction people includes that its boundary is calculated and identified comprising following calculating process：
Wherein, kmnIndicate the gray value of image pixel (m, n), K=max (kmn), shooting angle θmn∈ [0,1] utilizes Tr formula
Radian greyscale transformation is carried out to image：
R=2 ..., N, N are the natural number more than 2；
Wherein
Wherein θcFor Boundary Recognition threshold value, is tested and determined by face Boundary Recognition, then calculated as follows again：
Transformation coefficient k 'mn=(K-1) θmn
Then image boundary is extracted, the image boundary matrix extracted is
Edges=[k 'mn]
Wherein
k′mn=| k 'mn-min{k′ij}|,(i,j)∈W
W is 3 × 3 windows centered on pixel (i, j),
Then boundary judging result is verified, if identification enough, terminates, if being not enough to identify, to above-mentioned side
Boundary's recognition threshold is adjusted, and is repeated the above process, until obtaining good Boundary Recognition result, wherein Boundary Recognition threshold value takes
Value is ranging from [0.3,0.8]；
First judgment sub-unit judges that the factor includes face's posture, illuminance, has for being judged for the first time identification image
Unobstructed, face's distance is to carry out face's posture judgement first, carries out symmetry to identification image and integrity degree judges, to upper
The symmetry for stating the image of second step acquisition is analyzed, if symmetry meets predetermined threshold value requirement, then it is assumed that face is horizontal
Correct set, if it exceeds predetermined threshold value requires, then it is assumed that face's flat-hand position is incorrect, that is, side face occurs excessively or face inclines
Oblique over-education phenomenon, specific to judge algorithm to carry out binaryzation to obtained image, it is 80 to take threshold value, and the pixel more than 80 takes 0,
Remaining sets 1, is divided into the projection that left and right two parts seek horizontal direction respectively to the image after binaryzation, obtains two-part histogram
Figure calculates the chi-Square measure between histogram, and it is horizontal poorer to symmetry that chi-Square measure shows more greatly, then to face's integrity degree
Judged, i.e., to face's finite element inspection in the face mask that identifies, checking its eye, eyebrow, face, chin is
It is no to occur completely, if lacking some element or imperfect, then it is assumed that pitch angle is excessive when identification, and then to face, whether there is or not screenings
Gear judged, subsequent processing is carried out when unobstructed, finally to face distance whether properly judge, when be suitble to identification away from
From when, carry out subsequent processing, when the conditions are satisfied, carry out below step.
Second judgment sub-unit, the position for searching for crucial human face characteristic point in the specific region of face image, profit
With the grey level histogram segmentation of human eye candidate region in identification image, the partial pixel point that carrying out image threshold segmentation takes gray value minimum
Value be 255, the values of other pixels is 0, and second judgment sub-unit further includes pupil center's locator unit, for from
Pip is detected in two eye areas, the detection of eyes block is carried out using position and luminance information, from left and right eye region
The higher connection block of brightness is deleted in the image of binaryzation, selects the connection block positioned at extreme lower position as eyes block, and on
It further includes that low-power consumption processing subelement retains luminance component, obtain for carrying out chroma space to state Pupil diameter subelement
The luminance picture of human eye area enhances luminance picture into column hisgram linear equalization and contrast, then carries out threshold transformation,
Corrosion and expansion process are carried out to the image after threshold transformation, then Gauss is implemented in treated two-value human eye area
It is worth smothing filtering, threshold transformation is carried out again to the image after smooth, then carry out edge detection, ellipse fitting simultaneously detects in profile
Circle, the maximum circle of detection radius i.e. obtain the center of pupil；
Texture feature information obtains subelement and is handled facial recognition data after carrying out above-mentioned positioning, using high pass
Filter, the Gaussian function that graphics standard is melted into a zero-mean and unit variance is distributed, then carries out sub-block segmentation to image,
Dimension-reduction treatment calculates the two-value relationship of the gray value on each pixel value of image point adjacent thereto and secondly passes through respective pixel value
Point and weighting multiplied by weight, are then added the coding for foring local binary patterns, finally by the histogram using multizone
As the textural characteristics of image, Local textural feature calculation formula is as follows：
Hi,j=∑x,yI { h (x, y)=i } I { (x, y) ∈ Rj), i=0,1 ... n-1；J=0,1 ... D-1
Wherein Hi,jIndicate the region R divided from imagejIn belong to the number of i-th of histogram, n is local binary patterns
The number of statistical model feature, D is the areal of facial image, to the above- mentioned information of face key area and non-key area
It is counted, is then spliced, synthesis obtains the texture feature information of whole picture face image；
Compare and transmission subelement, for the texture feature information and face archives number to whole picture face image obtained above
It is compared and is transmitted according to the face texture characteristic information in library, to realize the augmented reality number based on low-power consumption recognition of face
According to acquisition and transmission.
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