CN104331412A - Method for carrying out face retrieval in normalized three-dimension face database - Google Patents

Method for carrying out face retrieval in normalized three-dimension face database Download PDF

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CN104331412A
CN104331412A CN201410490567.1A CN201410490567A CN104331412A CN 104331412 A CN104331412 A CN 104331412A CN 201410490567 A CN201410490567 A CN 201410490567A CN 104331412 A CN104331412 A CN 104331412A
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face
dimensional face
axis
feature
sample
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CN104331412B (en
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孔德慧
王茹
王立春
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Beijing University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • 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
    • G06V40/164—Detection; Localisation; Normalisation using holistic features
    • 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
    • G06V40/169—Holistic features and representations, i.e. based on the facial image taken as a whole

Abstract

The invention discloses a method for carrying out face retrieval in a normalized three-dimension face database. The method has the advantage that human event observation and information processing modes can be effectively simulated for building a more efficient and simpler face retrieval method and system. The method comprises the following steps that (1) model normalization is carried out: three-dimension face data is subjected to preprocessing of smoothening, denoising, shearing, coordinate correction and aligning, an obtained three-dimension face sample is to be used as a normalized three-dimension face, and the following model coordinate system is defined by a corresponding cylinder enveloping surface: a center axis of a cylinder is used as the Z axis, the direction passing through the nose tip point and being vertical to the Z axis is used as the Y axis, and the X axis is obtained through the multiplication cross of the Y axis and the Z axis; (2) the visual saliency is used as the measurement principle for carrying out sample saliency region definition and feature extraction; (3) the single-feature identification degree is used as the basis for feature weighing overlapping, the multi-feature fusion inter-face feature similarity measurement is realized, and the face retrieval is realized.

Description

A kind of method of carrying out face retrieval in normalization three-dimensional face storehouse
Technical field
The invention belongs to the technical field of CRT technology, relate to a kind of method of carrying out face retrieval in normalization three-dimensional face storehouse particularly.
Background technology
Data retrieval is intended to concentrate to obtain from data-oriented reach the sample data of specifying and requiring with the degree of conformity of querying condition.Specific to the problem of three-dimensional face retrieval, studying a question of core is the character representation of three-dimensional face model and the human face similarity degree measure that is associated with this feature.
Existing three-dimensional face search method is mainly extracted based on the geometric properties (comprising statistical nature and transform domain feature) of faceform and is carried out identifying.Conventional geometric properties comprises: face global information, as 3D grid; Face local feature region, characteristic curve; Merge the multi-modal fusion feature etc. of face two dimension and three-dimensional information.But the mankind and computing machine are being observed and the difference in computing power, determine the mankind observing, compare, when identifying face, be more the differentiation utilizing the strong visual signature of the intuitives such as the profile of face, face, the colour of skin, hair style to carry out object, but not calculate feature by the geometry being difficult to direct vision.In other words, geometry that above-mentioned existing retrieval technique adopts calculates feature, does not meet the mankind by observing psychology, physiology course when identifying its similar facial characteristics, causes the instability of result for retrieval, unreliable.
In fact, the development of face retrieval method depends on mode, the cost of human face data collection and Data Representation mode to a great extent.The cost that the development of computer hardware technique not only makes face information gather reduces, speed, precision and stored number improve greatly, and multiple vision sensor (comprising the depth transducer occurred in recent years) can be utilized to gather three-dimensional face information, set up multimodal three-dimensional face model storehouse.This be carry out more efficiently, more reliable and more stable face retrieval technique study provides data basis.
Summary of the invention
Technology of the present invention is dealt with problems and is: overcome the deficiencies in the prior art, a kind of method of carrying out face retrieval in normalization three-dimensional face storehouse is provided, it effectively can observe things and information processing manner by simulating human, builds more efficient simple face retrieval method.
Technical solution of the present invention is: this method of carrying out face retrieval in normalization three-dimensional face storehouse, and the method comprises the following steps:
(1) model normalization: by three-dimensional face data after the pre-service of level and smooth, denoising, shearing, coordinate rectification, alignment, obtain three-dimensional face sample as normalized three-dimensional face, rely on the model coordinate systems that the right cylinder enveloping surface corresponding to it is defined as follows: this cylindrical central shaft is as Z axis, through prenasale and the direction vertical with Z axis as Y-axis, X-axis is obtained by Y-axis and Z axis multiplication cross;
(2) be that tolerance principle carries out the definition of sample marking area and feature extraction: the significance value being obtained each pixel in three-dimensional face sample by formula (1) with vision significance:
S(p i)＝D(p i，p 1)+D(p i，p 2)+…+D(p i，p n) (1)
Wherein S (p i) be a p isignificance value, n is the number of pixel in image, D (... ...) represent attribute difference between two pixels;
(3) with single feature identification degree for according to carrying out characteristic weighing superposition, between the face realizing multiple features fusion, the similarity measurement of feature, realizes face retrieval.
Significance analysis method is applied to three-dimensional face searching field by the present invention, start with from the vision noticing mechanism of mankind itself, have studied definition and the feature interpretation retrieval in three-dimensional face to the salient region of significant effects, therefore effectively simulating human is observed things and information processing manner, is built more efficient simple face retrieval method.And compared with Euclidean distance, the similarity based on COS distance can consider the length of proper vector, the difference of aspect, direction two simultaneously, and be insensitive to the absolute figure of calculating, therefore be applicable to the tolerance of carrying out significant difference.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of this method.
Embodiment
As shown in Figure 1, this method of carrying out face retrieval in normalization three-dimensional face storehouse, the method comprises the following steps:
(1) model normalization: by three-dimensional face data after the pre-service of level and smooth, denoising, shearing, coordinate rectification, alignment, obtain three-dimensional face sample as normalized three-dimensional face, rely on the model coordinate systems that the right cylinder enveloping surface corresponding to it is defined as follows: this cylindrical central shaft is as Z axis, through prenasale and the direction vertical with Z axis as Y-axis, X-axis is obtained by Y-axis and Z axis multiplication cross;
(2) be that tolerance principle carries out the definition of sample marking area and feature extraction: the significance value being obtained each pixel in three-dimensional face sample by formula (1) with vision significance:
S(p i)＝D(p i，p 1)+D(p i，p 2)+…+D(p i，p n) (1)
Wherein S (p i) be a p isignificance value, n is the number of pixel in image, D (... ...) represent attribute difference between two pixels;
(3) with single feature identification degree for according to carrying out characteristic weighing superposition, between the face realizing multiple features fusion, the similarity measurement of feature, realizes face retrieval.
Significance analysis method introduce a kind of with the degree of strength of human attention irriate for foundation, define and measure the method for the feature to be identified of observed object.The method is attracting the different manifestations in human visual attention, generation vision difference by weighing different scene content (object), qualitative and even quantitatively scenario objects is composed with different significance value, thus mark off different regions (feature); And priority processing is carried out to highly significant region, and region lower for other conspicuousness is ignored as insignificant region or ignored.This Object Selection processing mode, makes the method have higher treatment effeciency.
Significance analysis method is applied to three-dimensional face searching field by the present invention, start with from the vision noticing mechanism of mankind itself, have studied definition and the description retrieval in three-dimensional face to the salient region of significant effects, therefore effectively simulating human is observed things and information processing manner, is built more efficient simple face retrieval method.And compared with Euclidean distance, the similarity based on COS distance can consider the length of proper vector, the difference of aspect, direction two simultaneously, and be insensitive to the absolute figure of calculating, therefore be applicable to the tolerance of carrying out significant difference.
Preferably, the color respectively with three-dimensional face in described step (2), the degree of depth and the significance value of normal direction three Representative properties to pixel are analyzed:
The color of face is mainly with the black composition of the kermesinus of the colour of skin, lip and supercilium, eye.For the conspicuousness of outstanding three-dimensional face color detects, the significance value that the color attribute of three-dimensional face is corresponding is obtained by formula (2):
S(p i)＝|RGB_ave-RGB_p i|/RGB_max (2)
Wherein, RGB_p ifor pixel p ired value component, RGB_max is the maximal value of all pixel redness, and RGB_ave is the mean value of all pixel red components; Along with the increase of threshold value, significance value is greater than specifies that " salient region " of threshold value shrinks gradually, in range set, and the supercilium of face sample, eye and mouth are marked as salient region all the time, represent that the significance value that these three regions obtain based on color approach is the highest, separating capacity is the strongest;
Similarly, the significance value that the depth attribute of three-dimensional face is corresponding is obtained by formula (3):
S(p i)＝Deep_p i/Deep_max (3)
Wherein, Deep_p ifor any pixel p idepth value, Deep_max is the maximal value of the degree of depth, and namely prenasale is to the distance of Y-axis initial point; For strengthening depth difference, above formula can increase zoom factor, shows by experiment, and along with the increase of degree of depth significance value, salient region shrinks to prenasale gradually;
Smooth surface has continually varying normal direction, is less than to the stimulation of visual attention the surface, local " cusp or projection " that normal direction is discontinuous change.And there is direct correlation in the region that the normal direction of face points changes greatly and the vision significance region of face, therefore, herein by the method for the cusp on this search smooth surface or projection in order to determine the region that three-dimensional face normal direction changes greatly, namely comprise the salient region of the parts such as nose, ear, mouth.For this reason, first introduce the concept of " method of average to ", namely rough for face curved surface is regarded as smooth surface, then each point normal direction on curved surface is averaged, the method for average that can obtain whole curved surface to.The method of average can regard the method direction approximately perpendicular to three-dimensional face curved surface " smooth " part as to Normal_ave.This conclusion can by appoint get position coordinates vector that two pixels on non-significant region are formed and the method for average to dot product close to 0 the fact verified.This conclusion and derivation can be expressed as follows.If:
λ = Normal _ ave · ( v i → - v j → ) - - - ( 4 )
Wherein, with any point p in three-dimensional face images iand p jpoint coordinate in XYZ coordinate system.
As 2 p iand p jbelong to same smooth region, and their distance is enough little relative to the distance of local feature size, so their normal direction value approximately equal, now the vector of their compositions and the method for average of curved surface to Normal_ave product close to 0, i.e. λ ≈ 0.Otherwise then λ > ε.
Therefore, the significance value that the normal direction attribute of three-dimensional face is corresponding is obtained by formula (5):
S ( p i ) = Σ p i , p j ∈ E Normal _ ave · ( v i → - c j → ) - - - ( 5 )
Wherein, p i, p jrepresent the pixel on three-dimensional face sample, E represents three-dimensional face curved surface area, v i, v jx ix jp respectively i, p jpoint coordinate, Normal_ave be this curved surface area the method for average to.By experiment, can find out, the region significance value that normal direction " fluctuation " is larger is higher, as ear, nose and oral area." fluctuation " is less like this, then significance value is less can to regard approximate smooth region as with face curved surface for eye and supercilium.
The present invention carries out specializing definition to the significance value of pixel with the color of three-dimensional face, the degree of depth and normal direction three Representative properties respectively.And on this basis, in conjunction with the advantage of variant feature and measurement results thereof, propose three-dimensional face significance analysis method based on multi-feature fusion.Single feature comprises the limitation of information due to it, fully can not meet our needs for high precision identification.Herein each information is normalized, after equilibrium assignmen weight, adopts the multiple features fusion strategy based on method for weighted overlap-add to carry out Face datection.The weights used determine according to color, the degree of depth, the recognition accuracy of normal direction three kinds of features when identifying separately respectively, namely uses the independent discrimination of each feature as contribution rate during fusion recognition.Obtained the final distance of two three-dimensional face samples by formula (6) in described step (2):
E ( m , n ) = Σ i = 1 k ω i E ( m , n ) i - - - ( 6 )
Wherein, E (m, n) represents the final distance of three-dimensional face sample m and sample n, E (m, n) ifor sample m, the n distance under notable feature i, use the COS distance of single feature to measure, k represents used notable feature sum, weights ω irepresent the proportion size of notable feature i in final identifying, its value is directly proportional to single feature discrimination of this feature.
Provide now a specific embodiment of the present invention.
In BJUT-3D face database used herein, totally 126 samples, 42 people (man 34, female 8), everyone comprises 3 groups of real neutral three-dimensional face data.
First, 126 Zhang San of BJUT-3D database are tieed up Nature face sample and are divided into three groups by us, often organize 42 samples, are assigned to from same successively in each group by three three-dimensional models that scanning person obtains.These three groups of number consecutivelies are A group, B group, C group by we.Do test set respectively successively by A, B, C group, all the other two groups are done training set, calculate sample distance and discrimination.Finally obtain 3 groups of experimental identification rates are averaged, is final accuracy.Alignd by all samples normalization due to BJUT-3D face database, we carry out the subregion of eyebrow, eye, nose, ear, mouth to human face region, then utilize the significance value of trying to achieve to calculate the distance in each region successively.
Experimental result is as shown in the table:
Supercilium Eye Nose Ear Oral area All
Color 53.97％ 56.03％ 66.98％ 68.25％ 58.73％ 88.10％
The degree of depth 25.40％ 46.83％ 57.94％ 38.89％ 48.41％ 69.05％
Normal direction 36.51％ 47.86％ 57.94％ 59.52％ 49.21％ 83.33％
Single feature recognition result accuracy of table 1 color, the degree of depth and normal direction
After the method according to formula (6) carries out Fusion Features, experimental result herein reaches the recognition accuracy of 91.27%.
Secondly, comprise 126 three-dimensional face sample databases be set to training set by whole.All samples individually as test set, are compared with training set successively, are used for calculating accuracy and recall rate.When accuracy is about 90% time, the recall rate of fusion method is close to 1.That is, when ensureing accuracy, correlated sampleses all in almost checkout system can be accomplished, reflect method in this paper good look into all-round power.
The above; it is only preferred embodiment of the present invention; not any pro forma restriction is done to the present invention, every above embodiment is done according to technical spirit of the present invention any simple modification, equivalent variations and modification, all still belong to the protection domain of technical solution of the present invention.

Claims (2)

1. in normalization three-dimensional face storehouse, carry out a method for face retrieval, it is characterized in that, the method comprises the following steps:
(1) model normalization: by three-dimensional face data after the pre-service of level and smooth, denoising, shearing, coordinate rectification, alignment, obtain three-dimensional face sample as normalized three-dimensional face, rely on the model coordinate systems that the right cylinder enveloping surface corresponding to it is defined as follows: this cylindrical central shaft is as Z axis, through prenasale and the direction vertical with Z axis as Y-axis, X-axis is obtained by Y-axis and Z axis multiplication cross;
(2) be that tolerance principle carries out the definition of sample marking area and feature extraction: the significance value being obtained each pixel in three-dimensional face sample by formula (1) with vision significance:
S(p i)＝D(p i，p 1)+D(p i，p 2)+…+D(p i，p n) (1)
Wherein S (p i) be a p isignificance value, n is the number of pixel in image, D (... ...) represent attribute difference between two pixels;
(3) with single feature identification degree for according to carrying out characteristic weighing superposition, between the face realizing multiple features fusion, the similarity measurement of feature, realizes face retrieval.
2. method of carrying out face retrieval in normalization three-dimensional face storehouse according to claim 1, it is characterized in that, the significance value of the color respectively with three-dimensional face in described step (2), the degree of depth and normal direction three Representative properties to pixel is analyzed: the significance value that the color attribute of three-dimensional face is corresponding is obtained by formula (2):
S(p i)＝|RGB_ave-RGB_p i|/RGB_max (2)
Wherein, RGB_p ifor pixel p ired value component, RGB_max is the maximal value of all pixel redness, and RGB_ave is the mean value of all pixel red components;
The significance value that the depth attribute of three-dimensional face is corresponding is obtained by formula (3):
S(p i)＝Deep_p i/Deep_max (3)
Wherein, Deep_p ifor any pixel p idepth value, Deep_max is the maximal value of the degree of depth;
The significance value that the normal direction attribute of three-dimensional face is corresponding is obtained by formula (5):
S ( p i ) = Σ p i , p j ∈ E Normal _ ave · ( v i → - c j → ) - - - ( 5 ) Wherein, p i, p jrepresent the pixel on three-dimensional face sample, E represents three-dimensional face curved surface area, v i, v jp respectively i, p jpoint coordinate, Normal_ave be this curved surface area the method for average to.Method of carrying out face retrieval in normalization three-dimensional face storehouse according to claim 1, it is characterized in that, based on multiple features fusion strategy in described step (3), with single feature discrimination for feature weight, obtained the final distance of two three-dimensional face samples by formula (6):
E ( m , n ) = Σ i = 1 k ω i E ( m , n ) i - - - ( 6 ) Wherein, E (m, n) represents the final distance of three-dimensional face sample m and sample n, E (m, n) ifor sample m, the n distance under notable feature i, use the COS distance of single feature to measure, k represents used notable feature sum, weights ω irepresent the proportion size of notable feature i in final similarity measurement process, its value is directly proportional to single feature discrimination of this feature.
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CN104765768A (en) * 2015-03-09 2015-07-08 深圳云天励飞技术有限公司 Mass face database rapid and accurate retrieval method
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