CN1687957A - Man face characteristic point positioning method of combining local searching and movable appearance model - Google Patents
Man face characteristic point positioning method of combining local searching and movable appearance model Download PDFInfo
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Abstract
The invention is a method for locating characteristic points of a human face, integrated with local search and active appearance model, firstly using a part of a picture of human face with the coordinates of characteristic points as a sample to build a new picture of human face for detecting, so as to obtain a rectangular region containing the human face, locating eyes and mouth in the rectangular region, using the positions of the eyes and mouth as initial positions, searching by the active appearance model, and finally finding many characters points of human face, and thus completing the integral location of the characteristic points of human face. The method can be further applied to recognizing human face, sex, and expression, estimating age, and other aspects.
Description
Technical field
The present invention relates to a kind of recognition of face that is applied to, Expression Recognition, sex identification, the man face characteristic point positioning method of the combining local searching of estimation of Age and movable appearance model (AAM), this method relates to fields such as Flame Image Process, mathematical modeling, statistical study.
Background technology
It is the most key technology during recognition of face, Expression Recognition, sex identification, estimation of Age etc. are used that human face characteristic point detects, the accuracy of its characteristic point position location directly has influence on the precision of identification, therefore, the position of locating human face characteristic point exactly can be improved the precision of identification widely.Human face characteristic point mainly comprises the marginal point on the pupil center of circle, canthus, the corners of the mouth, nose, the lower jaw, but put iff relying on these that to carry out recognition of face be far from being enough, therefore also must further find some other human face characteristic point, such as: eyebrows, eyebrow tail, eyebrow peak, the bridge of the nose, lip paddy, lip peak are very difficult yet find these all unique points simultaneously.
Find by prior art documents, (Rein-Lien Hsu such as Rein-Lien Hsu, MohamedAbdel-Mottaleb, Anil K.Jain Face Detection In Color images IEEETRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL 24, NO.5.MAY 2002) once the statistics by great amount of samples provided the distribution of eye areas at each component of YCbCr color space, and locate the Position Approximate of human eye and face roughly according to the distribution of these components, but the number of the unique point that these class methods find very little, do not reach the number of the desired unique point of recognition of face far away.(T.Cootes such as T.Cootes, G.Edwards, and C.Taylor, " Active AppearanceModels; " IEEE Trans.Pattern Analysis and Machine Intelligence, vol.23, no.6, pp.681-685, June 2001.) method that proposed a kind of AAM (movable appearance model) carries out the face characteristic point location, at first manual some unique points of on a part of facial image, demarcating of such global characteristics point searching method, with the method for statistics these characteristic point positions of having demarcated are analyzed then, obtained a general average shape model, then on each facial image, carry out pixel sampling with this shape, carry out statistical study simultaneously and obtain a texture model, then shape and texture model are carried out statistical study and obtain final display model.But this method has very big dependence to the initial position of display model selected, if the initial position of display model is near the unique point of expectation, this model can be very easy to find the unique point of expectation, and iterations is considerably less, if but this display model is away from the characteristic point position of expectation, this model needs a large amount of iterationses just can find the characteristic point position of expectation so, sometimes may be absorbed in the position of a mistake, thereby can not provide correct positioning.
Summary of the invention
The objective of the invention is to overcome deficiency of the prior art, the man face characteristic point positioning method of a kind of combining local searching and movable appearance model is provided.Make the localization method of the new human face characteristic point fast that its this two class methods combine, not only improved feature and counted but also strengthened robustness.Make the man face characteristic point positioning method of its foundation, can be used for recognition of face, sex identification, Expression Recognition, fields such as estimation of Age.
The present invention is achieved by the following technical solutions, the present invention sets up movable appearance model with a part of facial image as sample earlier, the detection of people's face then, the location of eyes and face, the new facial image of one width of cloth is carried out people's face detecting operation, obtain a rectangular area that comprises people's face, on this zone, carry out the location of eyes and face, position with eyes and face is an initial position, search for movable appearance model, finally find numerous human face characteristic points, so just finish the integral body location of human face characteristic point.
The foundation of described movable appearance model, be meant: at first in face database, select a part of n facial image randomly and k unique point of manual demarcation in selected image, just obtain an one-dimensional vector at each image like this, this vector has 2k element, the x coordinate of preceding k unique point of k element representation, the y coordinate of a back k element representation k unique point, n image is with regard to corresponding n such vector, this n vector is carried out treatment for correcting, carry out the PCA processing then and obtain an average shape model mean_shape and Bs; Then with mean_shape as target shape, set up in each training sample image the corresponding relation between the unique point in the unique point and average shape, and use deformation method that the original image face area is deformed to target shape based on corner block with this corresponding relation, simultaneously the image after each distortion is carried out pixel sampling, obtain a texture vector, this n texture vector carried out PCA handle, obtain average texture model mean_tex, and Bg; Because Bs is the matrix of m*n, Bg is the matrix of h * n, they are formed the matrix B of (m+h) * n, this matrix is carried out PCA handle, obtain the matrix B a of an a*n, a is much smaller than (m+h), each column vector of Ba is represented the character shape of the image corresponding with it and the situation of change of gray scale, and just the appearance change situation has so just obtained a movable appearance model, be AAM, so just finished the foundation of model.
Described shape, be meant: this n vector is passed through affined transformation, comprise rotation, translation, convergent-divergent, make itself and first vector the most approaching, so just obtained n new vector, calculated the average of this n new vector, on average made itself and first vector the most approaching by affined transformation to this, obtain one new average, then with this average out to benchmark shape, n new vector by affined transformation make itself and this on average approaching, repeat this process, up to convergence, so just obtained the vector of final n expression shape, they have been formed the matrix of a 2k * n, this matrix has been carried out pivot analysis (PCA) handle, obtain the matrix B s of a m * n and the vectorial mean_shape of 2k * 1, m is much smaller than 2k, and each column vector of Bs is represented the situation of change of unique point coordinate in the image corresponding with it, and mean_shape represents the average of this n vector, just average shape has so just obtained a shape;
Described texture model, be meant: just obtain the texture vector of a correspondence through the image after being out of shape for each, each vector has s element, each element representation corresponding gray, n corresponding n the such vector of image, the matrix of this n-tuple being formed a s * n, this matrix is carried out PCA to be analyzed, obtain the matrix B g of a h * n and the vectorial mean_tex of s * 1, h is much smaller than s, and each column vector of Bg is represented the situation of change of the image pixel gray-scale value corresponding with it, and mean_tex represents the average of this n vector, just average texture has so just obtained a texture model.
Described people's face detects, the location of eyes and face, be meant: great amount of samples is carried out statistical study, provide the colour of skin, eyes and the face distribution situation in the YCbCr space, at first find possible area of skin color according to the distribution of the colour of skin, according to the distribution situation of eyes and face each possible area of skin color is analyzed then, if certain regional existing eye has face again, just think that this zone is a human face region, so both finish the detection of people's face, provided the Position Approximate of eyes and face again.
Described position with eyes and face is an initial position, search for movable appearance model, be meant: with the position of eyes and face initial searching position as display model, use the continuous iteration of movable appearance model searching method then, on facial image, find the position of mating the most with display model, this position is the position of finally finding, and has so just finished the search of full feature point group.
Method of the present invention can obtain higher accuracy rate and very fast speed.Owing to utilize method for detecting human face to orient human face region, location eyes and face on this zone, with the initial position that the position of eyes and face is searched for as AAM, this has just further improved the speed of positioning feature point under the prerequisite of not losing the initial alignment precision.This invention is to combine the advantage that the high and movable appearance model searching method of local feature point searching method precision can be searched for a large amount of unique points and have good robustness.
Embodiment
Below in conjunction with specific embodiment technical scheme of the present invention is described in further detail.
The facial image that embodiment adopts comes in the facial image database of taking.Whole invention implementation procedure is as follows:
1. set up the AAM model.From face database, select n image as training sample, k unique point of manual demarcation on selected facial image, these unique points in each image are formed a vector, this n vector is passed through affined transformation, comprise rotation, translation, convergent-divergent, make itself and first vector the most approaching, so just obtained n new vector, calculate the average of this n vector, on average make itself and first vector the most approaching to this by affined transformation, obtain one new average, then with this average out to benchmark shape, n the vector by affined transformation make itself and this on average approaching, repeat this process, up to convergence, so just obtained the vector of final n expression shape, they have been formed the matrix of a 2k * n, this matrix has been carried out pivot analysis (PCA) handle, obtain the matrix B s of a m * n and the vectorial mean_shape of 2k * 1, m is much smaller than 2k, and each column vector of Bs is represented the situation of change of unique point coordinate in the image corresponding with it, and mean_shape represents the average of this n vector, just average shape has so just obtained a shape; Then with mean_shape as target shape, each image in the training sample is set up in this sample image the corresponding relation between the unique point in the unique point and average shape, and use deformation method that the original image face area is deformed to target shape based on corner block with this corresponding relation, simultaneously the image after each distortion is carried out pixel sampling, obtain a texture vector, each vector has s element, each element representation corresponding gray, n image is with regard to corresponding n such texture vector, this n vector is carried out PCA to be handled, obtain the matrix B g of a h * n and the vectorial mean_tex of s * 1, h is much smaller than s, each column vector of Bg is represented the situation of change of the image pixel gray-scale value corresponding with it, mean_tex represents the average of this n vector, just average texture has so just obtained a texture model.Because Bs is the matrix of m*n, Bg is the matrix of h * n, they are formed the matrix B of (m+h) * n, this matrix is carried out PCA handle, obtain the matrix B a of an a*n, a is much smaller than (m+h), each column vector of Ba is represented the character shape of the image corresponding with it and the situation of change of gray scale, and just therefore the appearance change situation has just obtained a movable appearance model, be AAM, so just finished the foundation of model.
2. a large amount of facial image samples are carried out statistical study, provide the colour of skin, each comfortable YCbCr spatial distributions situation of eyes and face, be that they are at the residing threshold range of YCbCr color space [minskinmaxskin], [mineye maxeye] and [minmouth maxmouth], for each pixel in the image, calculate its value val in the YCbCr space, and judge which threshold range it belongs to, for example val is in [mineye maxeye] this scope and just it is judged to be eye areas, by that analogy, like this each pixel in the image is all finished such operation and also just can provide the colour of skin, the Position Approximate of eyes and face.
With the Position Approximate of eyes and face as the AAM initial position, search for the AAM searching method, find the final position of numerous human face characteristic points.The eyes that find with previous step and the Position Approximate of face are as the initial position of AAM model, the movable appearance model of having set up is placed on initial position, fit one with movable appearance model image of a size and it is converted to a vector with this model, and calculate the difference diff of this vector and average texture mean_tex, then by affined transformation (convergent-divergent, translation, rotation) adjustment of parameter diminishes diff and among the Ba, repeat above-mentioned steps until diff less than certain threshold value, so just finish the search procedure of AAM, also just finished the location of unique point.
Claims (6)
1, the man face characteristic point positioning method of a kind of combining local searching and movable appearance model, it is characterized in that, set up movable appearance model with a part of facial image that has the unique point coordinate position as sample earlier, carrying out people's face then detects, the location of eyes and face, the new facial image of one width of cloth is carried out people's face detecting operation, obtain a rectangular area that comprises people's face, on this zone, carry out the location of eyes and face, position with eyes and face is an initial position, search for movable appearance model, finally find numerous human face characteristic points, so just finish the integral body location of human face characteristic point.
2, the man face characteristic point positioning method of combining local searching according to claim 1 and movable appearance model, it is characterized in that, the foundation of described movable appearance model, be meant: at first in face database, select n facial image randomly and k unique point of manual demarcation in selected image, just obtain an one-dimensional vector at each image like this, this vector has 2k element, the x coordinate of preceding k unique point of k element representation, the y coordinate of a back k element representation k unique point, n image is with regard to corresponding n such vector, this n vector is carried out affined transformation and calibration process, carrying out PCA then handles, obtain a shape, this shape comprises the matrix B s of one m * n and the vectorial mean_shape of 2k * 1; Then with mean_shape coordinate target shape, set up in each training sample image the corresponding relation between the unique point in the unique point and average shape, and use deformation method that the original image face area is deformed to target shape based on corner block with this corresponding relation, simultaneously the image after each distortion is carried out pixel sampling, obtain a texture vector, n image is with regard to corresponding n such vector, this n vector is carried out PCA to be handled, obtain a texture model, this texture model comprises the matrix B g of one h * n and the vectorial mean_tex of s * 1; Because Bs is the matrix of m*n, Bg is the matrix of h * n, they are formed the matrix B of (m+h) * n, this matrix is carried out PCA handle, obtain the matrix B a of an a*n, a is much smaller than (m+h), each column vector of Ba is represented the character shape of the image corresponding with it and the situation of change of gray scale, and just the appearance change situation has so just obtained a movable appearance model, be AAM, so just finished the foundation of model.
3, the man face characteristic point positioning method of combining local searching according to claim 2 and movable appearance model, it is characterized in that, described shape, be meant: this n vector is passed through affined transformation, comprise rotation, translation, convergent-divergent, make itself and first vector the most approaching, so just obtained n new vector, calculate the average of this n vector, on average make itself and first vector the most approaching to this by affined transformation, obtain one new average, then with this average out to benchmark shape, the new vector of n by affined transformation make itself and this on average approaching, repeat this process, up to convergence, so just obtained the vector of final n expression shape, they have been formed the matrix of a 2k * n, this matrix has been carried out pivot analysis handle, obtain the matrix B s of a m * n and the vectorial mean_shape of 2k * 1, m is much smaller than 2k, and each column vector of Bs is represented the situation of change of unique point coordinate in the image corresponding with it, and mean_shape represents the average of this n vector, just average shape has so just obtained a shape.
4, the man face characteristic point positioning method of combining local searching according to claim 2 and movable appearance model, it is characterized in that, described texture model, be meant: the vector that just obtains a correspondence for each distortion back image, each vector has s element, each element representation corresponding gray, n corresponding n the such vector of image, the matrix of this n-tuple being formed a s * n, this matrix is carried out PCA to be analyzed, the matrix B g of a h * n who gets and the vectorial mean_tex of s * 1, h is much smaller than s, and each column vector of Bs is represented the situation of change of the image pixel gray-scale value corresponding with it, and mean_tex represents the average of this n vector, just average texture has so just obtained a texture model.
5, the man face characteristic point positioning method of combining local searching according to claim 1 and movable appearance model, it is characterized in that, described people's face detects, the location of eyes and face, be meant: great amount of samples is carried out statistical study, provide the colour of skin, eyes and the face distribution situation in the YCbCr space, at first find possible area of skin color according to the distribution of the colour of skin, according to the distribution situation of eyes and face each possible area of skin color is analyzed then,, just thought that this zone is a human face region if certain regional existing eye has face again, so both finish the detection of people's face, provided the Position Approximate of eyes and face again.
6, the man face characteristic point positioning method of combining local searching according to claim 1 and movable appearance model, it is characterized in that, described position with eyes and face is an initial position, search for movable appearance model, be meant: with the position of eyes and face initial searching position as display model, use the continuous iteration of movable appearance model searching method then, on facial image, find the position of mating the most with display model, this position is the position of finally finding, and has so just finished the search of full feature point group.
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| CN100349173C (en) * | 2005-12-15 | 2007-11-14 | 上海交通大学 | Method for searching new position of feature point using support vector processor multiclass classifier |
| CN100414562C (en) * | 2006-10-10 | 2008-08-27 | 南京搜拍信息技术有限公司 | Method for positioning feature points of human face in human face recognition system |
| CN100444191C (en) * | 2006-11-08 | 2008-12-17 | 中山大学 | Multiple expression whole face profile testing method based on moving shape model |
| CN101635028A (en) * | 2009-06-01 | 2010-01-27 | 北京中星微电子有限公司 | Image detecting method and image detecting device |
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| CN101093542B (en) * | 2006-02-15 | 2010-06-02 | 索尼株式会社 | Inquiry system, imaging device, inquiry device, information processing method |
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| CN101393599B (en) * | 2007-09-19 | 2012-02-08 | 中国科学院自动化研究所 | Game role control method based on human face expression |
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| CN106529397A (en) * | 2016-09-21 | 2017-03-22 | 中国地质大学（武汉） | Facial feature point positioning method and system in unconstrained environment |
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