Apparatus and method of pattern recognition

In one embodiment of the invention, a pattern recognition apparatus comprises a unit for inputting a pattern of a to-be recognized category; and a processor with a memory for: generating input subspace; calculating and storing reference subspaces; storing constraint subspaces for extracting features; projecting the input subspace and the reference subspaces respectively onto the constraint subspaces; calculating similarities between the respective reference subspaces and the input subspace in such projected state; combining the similarities in respect of the constraint subspaces on each of the reference subspaces; and identifying the to-be recognized category with a category corresponding to one of the reference subspaces, if the combined similarity between the one of reference subspace and the input subspace is highest among them.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority from the prior Japanese Patent Application No. 2003-376267, filed on Nov. 5, 2003; the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

This invention relates to an art that extracts effective features for the pattern recognition, and thereby carries out stably the pattern recognition.

The pattern recognition art that determines a category of the unknown pattern is needed in various fields. As one of the pattern recognition art, Watanabe et al (S. Watanabe, N. Pakvasa, Subspace method of pattern recognition, Proc. 1st Int. J. Conf. on Pattern Recognition, 1973) propose the subspace method. The subspace method is advantageous in that feature extraction and classification can be executed at the same time and extension is easy from two categories to a plurality of categories. In the subspace method, a similarity is determined by angle between an input vector converted from an unknown pattern and a reference subspace. The reference subspace is generated by the principal component analysis from a previously obtained vector of one category. When the similarity is equal to or greater than a threshold, the input vector can be determined the category.

JP-A-11(1999)-265452 and Maeda et al (K. Maeda, T. Watanabe, A Pattern Matching Method with Local Structure, IEICE Trans. D-II Vol. J68-D, No. 3, 345-352, 1985) propose the mutual subspace method which determines similarity by angle between the input subspace and the reference subspace. The mutual subspace method is more robust against pattern variations and noise because of using an input subspace instead of an input vector. The similarity S between subspace P and subspace Q is calculated by the following equation.
S=cos2θ  (1)
where θ represents the angle between P and Q. This angle is called canonical angle.

If two subspaces are equal, then θ=0. Described in JP-A-11(1999)-265452 cited before, cos2θ is obtained by determining a maximum eigenvalue of the following matrix X.

Xa=λ⁢⁢a(2)X=(xij),(i,j=1~N)(3)(xij)=∑1≤k≤N⁢(ψi,ϕk)⁢(ϕk,ψj)(4)
where ψirepresents an i-th basic vector on the subspace P. φjrepresents an j-th basic vector on the subspace Q. N represents the number of dimensions of the subspace.

Furthermore, in order to enhance the recognition accuracy for the mutual subspace method, JP-A-2000-30065 and Fukui et al (K. Fukui, O. Yamaguchi, K. Suzuki, K. Maeda, Face Recognition under Variable Lighting Condition with Constrained Mutual Subspace Method—Learning of Constraint Subspace to Reduce Influence of Lighting Changes—, IEICE Trans. D-II Vol. J82-D-II, No. 4, 613-620, 1999) propose the constrained mutual subspace method. This technique is that the input subspace and the reference subspace are projected onto a constraint subspace for emphasizing extra-category variation which is considered effective for the pattern recognition. The similarity Scunder the constrained mutual subspace method, determined by an angle θcbetween subspace Pcand subspace Qcwhich are projected onto a constraint subspace C (Equation (5)).
SC=cos2θC(5)

The procedure of projection onto a constraint subspace is detailed in JP-A-2000-30065 and Maeda et al cited on p.2. The procedure of generating a constraint subspace is described in the JP-A-2000-30065.

When the constraint subspace is used for the pattern recognition, recognition performance becomes unstable because the similarity of a certain category becomes low. If the constraint subspace is changed, the similarity of another category becomes low. For example on the face image recognition system, the person who is occurred with such problem is prone to higher false rejection rate than other persons.

SUMMARY OF THE INVENTION

Therefore, the present invention proposes a method of using a plurality of constraint subspaces for the pattern recognition. By using a plurality of constraint subspaces, the influence of the above problem is diminished. It is expected to decrease the false rejection rate greatly.

Therefore, the present invention provides a pattern recognition apparatus capable of carrying out stable pattern recognition, and a method for the same.

According to one embodiment of the present invention, a pattern recognition apparatus comprises: a unit for inputting at least two input patterns; and a processor with a memory for; generating an input subspace from the input patterns; storing reference subspaces which are generated from reference patterns; storing a plurality of constraint subspaces for extracting an effective feature for pattern recognition; projecting the input subspace and the reference subspaces onto each one of the constraint subspaces; calculating similarities between thus projected input subspace and thus projected reference subspaces, on the each one of constraint subspaces; combining the similarities obtained by using the plurality of constraint subspaces, as to find a combined similarity between the input subspace and each of the reference subspaces; and determining a category of the input subspace by comparing the combined similarities.

Such construction enables correct pattern recognition in respect of the category of input subspace by using a plurality of constraint subspaces.

According to another embodiment of the present invention, a pattern recognition apparatus comprises: a unit for inputting an input pattern; and a processor with a memory for; generating an input vector from the input pattern; storing reference subspaces which are generated from reference patterns; storing a plurality of constraint subspaces for extracting an effective feature for pattern recognition; projecting the input vector and the reference subspaces onto each one of the constraint subspaces; calculating similarities between thus projected input vector and thus projected reference subspaces, on the each one of constraint subspaces; combining the similarities obtained by using the plurality of constraint subspaces, as to find a combined similarity between the input vector and each of the reference subspaces; and determining a category of the input subspace by comparing the combined similarities.

Such construction enables correct pattern recognition in respect of the category of input vector by using a plurality of constraint subspaces.

By the above embodiments, the pattern recognition is stably executed by utilizing a plurality of feature extractions in order to obtain effective information for the pattern recognition.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a method of using a plurality of constraint subspaces and a learning method of constraint subspaces from learning patterns. These methods are a technical idea proposed for the first time in the present invention. This is newly termed as a “multiple constrained mutual subspace method”. Concerning this, descriptions of using a plurality of constraint subspaces are made in the following first to fourth embodiments. The learning method of constraint subspaces will be described in the fifth and sixth embodiments.

First Embodiment

In method of using a plurality of constraint subspaces, a similarity combining is needed. The similarity combining methods are divided into two, depending upon whether to combining those based on a fixed weight, or to combining those based on a dynamic weight. At first, the former method of recognition is described as a first embodiment.

The present embodiment is shown as a pattern recognition apparatus100inFIG. 1. The pattern recognition apparatus100comprises a pattern input unit101, an input subspace generating unit102, a reference subspace storing unit103, a constraint subspace storing unit104, a subspace projecting unit105, a similarity calculating unit106, a similarity combining unit107, and a determining unit108. Incidentally, the subspace projecting unit105-1, . . . ,105-M and the similarity calculating unit106-1, . . . ,106-M each exist in the number of M of the constraint subspaces stored in the constraint subspace storing unit104. Meanwhile, the units101-108are to be realized by a program stored on a computer.

The pattern input unit101acquires patterns of an unknown category, and stores it to a memory after conversion into a feature vector. Pattern acquisition maybe at all times. Note that the “pattern” refers to physical information capable of specifying a category, which includes face image, fingerprints, voice, characters and DNA.

The input subspace generating unit102, when feature vectors are acquired in a predefined number, generates an input subspace based on principal component analysis.

The reference subspace storing unit103stores in the number R of reference subspaces which are generated by the principal component analysis.

Each of the subspace projecting units105projects the input subspace and reference subspaces stored in the reference subspace storing unit103, onto one of the constraint subspaces stored in the constraint subspace storing unit104. The procedure of projection is detailed in JP-A-2000-30065 and Maeda et al cited on p. 2.

Each of the similarity calculating unit106calculates similarities between the reference subspaces and the input subspace in a state having been projected onto one of the constraint subspaces, by the mutual subspace method.

The similarity combining unit107takes an average value, maximum value, minimum value or median value from a plurality of similarities obtained by using constraint subspaces (hereinafter, referred to as a combined similarity)

In this description, calculating an average, etc. of similarities for each reference subspace is referred to as “combining”.

When the combining is made by taking the average value, a similarity SEis determined by Equation (6).

SE=1M⁢∑1≤i≤M⁢cos2⁢θCi(6)
where M represents the number of constraint subspaces, and θcirepresents an angle between Pciand Qciwhich are the subspace P, Q projected onto a constraint subspace Ci. Meanwhile, when the combining is made by taking a maximum value, a minimum value or a median value, the similarities SEcan be respectively determined by the following equation.
SE=max{cos2θC1. . . , cos2θCM}  (7)
SE=min{cos2θC1. . . , cos2θCM}  (8)
SE=median{cos2θC1. . . , cos2θCM}  (9)

Besides, there is a method that, after selecting candidates by the constrained mutual subspace method using each constraint subspaces, category is finally determined by decision-by-majority or logical sum from the candidates.

Incidentally, combining the similarities is carried out by the number R of the reference subspaces stored in the reference subspace storing unit103. Each combined similarity represents a similarity between the reference subspace and the input subspace.

The determining unit108determines the category of the input subspace from the combined similarity which is the highest value and greater than a preset threshold.

Second Embodiment

Now a second embodiment is explained on the basis ofFIG. 2.

The first embodiment was on the former of the foregoing similarity combining methods. Now explained is, as a second embodiment, the latter method that combines similarities through a dynamic weight relying upon the input/reference subspaces.

This embodiment is shown as a pattern recognition apparatus200inFIG. 2.

The pattern recognition apparatus200comprises various units201-208functioning similarly to the units101-108of the first embodiment, and a weight calculating unit209. Incidentally, the subspace projecting unit205and the similarity calculating unit206each exist in the number of M of the constraint subspaces stored in the constraint subspace storing unit204similarly to the first embodiment.

The weight calculating unit209selects an optimal constraint subspace for the input/reference subspace from a plurality of constraint subspaces, or makes a weighting with an adaptation of the input/reference subspace to the constraint subspace. In this case, the similarity SEin the similarity combining unit207is in a weighed sum, to be determined by Equation (10).

SE=∑1≤i≤M⁢ωi⁢cos2⁢θCi(10)∑1≤i≤M⁢ωi=1(11)
where wi (1≦i≦M) represents a weight on each constraint subspace (total sum is assumably 1.0).

As for how to calculate a weight wi in the weight calculating unit209, it can be considered to use an angle θC1ibetween the input/reference subspace and a subspace C1idefined by the learning patterns for the constraint subspace. This is based on the fact that recognition accuracy increases when the variation of learning patterns and the variation of input/reference patters are similar. The subspace C1iis generated from learning patterns of the constraint subspace C1. In this case, weight wi can be expressed by Equation (12).

So far explained is the second embodiment.

Third Embodiment

Now a third embodiment is explained on the basis ofFIG. 3.

The similarity calculating unit106of the first embodiment, although using the mutual subspace method, can be replaced with a similarity calculating unit using the subspace method. This case is described as a third embodiment.

The present embodiment is shown by a pattern recognition apparatus300inFIG. 3. The pattern recognition apparatus300comprises a pattern input unit301, a reference subspace storing unit302, a constraint subspace storing unit303, projecting units304, similarity calculating units305, a similarity combining unit306, and a determining unit307. In the apparatus, number of the projecting units304and the number of the similarity calculating units305are identical with the number (“M”) of the constraint subspaces stored in the constraint subspace storing unit303.

The pattern input unit301has the similar function to the pattern input unit101of the first embodiment.

The reference Subspace Storing Unit302has the similar function to the reference Subspace Storing Unit102of the first embodiment.

Each of the projecting input vector and units304projects the reference subspaces in the number of R stored in the reference subspace storing unit302, onto one of the constraint subspaces stored in the constraint subspace storing unit303.

Each of the similarity calculating units305calculates similarities between the input vector and the reference subspace projected onto one of the constraint subspace, by the subspace method.

The similarity combining unit306as the similar function to the determining unit107of the first embodiment

The determining unit307has the similar function to the determining unit108of the first embodiment.

Fourth Embodiment

Now a fourth embodiment is explained on the basis ofFIG. 4.

In the second embodiment, replacement is similarly possible with a similarity calculating unit using the subspace method based. This case is explained as a fourth embodiment.

The present embodiment is shown as a pattern recognition apparatus400inFIG. 4. The pattern recognition apparatus400comprises units401-407functioning similarly to the units301-307of the third embodiment, and a weight calculating unit408. In the apparatus, number of the projecting units404and the number of the similarity calculating units405are identical with the number (“M”) of the constraint subspaces stored in the constraint subspace storing unit403, similarly to the third embodiment.

The weight calculating unit408selects an optimal constraint subspace for the input vector or reference subspace from a plurality of constraint subspaces, or makes a weighting with an adaptation of an input vector or reference subspace and constraint subspace.

So far explained is the fourth embodiment.

Incidentally, the pattern recognition apparatus100,200,300,400may store a program for realizing the functions on a storage medium of HDD, FDD, CD, DVD, memory or the like, to be invoked onto the computer when carrying out a recognition.

Fifth Embodiment

Now a fifth embodiment is explained on the basis ofFIG. 5.

The fifth and sixth embodiments describe means of efficiently leaning a plurality of constraint subspaces from leaning patterns.

H. Aso et al (H. Aso, K. Tsuda, N. Murata, statistics for the pattern recognition and the machine learning, Iwanami Shoten, 2003) describes ensemble learning, including bugging and boosting as a representative method. In bugging, sampling is repeatedly made from learning patterns, to learn a plurality of classifiers, in boosting; the next classifier is generated by giving weights to the mistaken patterns in the previous classifier. The below describes a learning method of constraint subspaces in the framework of ensemble learning.

At first, the fifth embodiment describes an introduction of the bugging framework to learn constraint subspaces.

The present embodiment is shown as constraint subspaces learning apparatus500inFIG. 5. The constraint subspaces learning apparatus500comprises a learning subspaces storing unit501, a learning subspace selecting unit502, a constraint subspace learning unit503and a constraint subspace storing unit504.

The learning subspace storing unit501stores learning subspaces in the number of K prepared for ensemble learning. The learning subspaces are generated by a principal component analysis from learning patterns belonging to the category.

The learning subspace selecting unit502randomly selects learning subspaces in the number of T from the learning subspace storing unit501. Incidentally, when the learning subspaces of the same category are included among the learning subspaces in the number of T, selection is made once again.

The constraint subspace learning unit503generates a constraint subspace from the learning subspaces in the number of T, by the method described in JP-A-2000-30065 and Fukui et al cited on p. 2.

The learning subspace selecting unit502and the constraint subspace learning unit503are repeatedly used until constraint subspaces in the number of M are stored to the constraint subspace storing unit504.

Sixth Embodiment

Now a sixth embodiment is explained on the basis ofFIG. 6.

The sixth embodiment concerns an introduction of a boosting framework to learn constraint subspaces.

The present embodiment is shown as a constraint subspace learning apparatus600inFIG. 6. The constraint subspace learning apparatus600comprises a learning subspace storing unit601, a learning weight calculating unit602, a constraint subspace learning unit603and a constraint subspace storing unit604.

The learning subspace storing unit601has the same function as the learning subspace storing unit501.

The learning weight calculating unit602determines a weight Dt(j) of a learning subspace Pj, for generating the constraint subspace in the constraint subspace learning unit603. The newest weight Dt(j) is determined by the following equation, by using the newest constraint subspace Ct-1stored in the constraint subspace storing unit604.

Dt⁡(j)=Sj′∑1≤j≤K⁢Sj′(13)Sj′=∑1≤k≤K,i≠k⁢cos2⁢θCt-1jk(14)
where θct-1jkrepresents an angle between the learning subspace Pjand Pkafter projection onto the constrained subspace Ct-1. Due to this, the learning subspaces which are similar on the constraint subspace Ct-1have an increasing weight. In the next constraint subspace Ct, the similar learning subspaces no longer become similar. Equation (14) may be introduced with a condition that a sum is taken from the angle θct-1jkequal to or greater than a definite threshold. Incidentally, the initial weight is assumably given D1(j)=1/K.

The constraint subspace learning unit603generates the constraint subspace by the method as described in JP-A-2000-30065 and Fukui et al cited on p. 2, after multiplying the weight Dt(j) on basic vector of a learning subspace Pj.

The learning weight calculating unit602and the constraint subspace learning unit603are repeatedly used until constraint subspaces in the number M are stored to the constraint subspace storing unit604.

One of the methods of using the constraint subspaces which are made in the sixth embodiment, similarities are combined through the use of reliability. In this case, the similarity combining unit107of the first embodiment uses Equation (15).

The reliability αtmay be a ratio that the angle θct-1jkof Equation (14) does not exceed a constant threshold. Otherwise, instead of using reliability, similarities may be combined by a method with an average value as described in the similarity combining unit107.

Detailed Example of the First Embodiment

Now explained is pattern recognition using face images, as a detailed example of the first embodiment. The flow of this detailed example is shown inFIG. 7. InFIG. 8is shown a configuration of the face image recognition apparatus800.

The face image recognition apparatus800comprises a face input unit801, an input subspace generating unit802, a reference subspace storing unit803, a constraint subspace storing unit804, a subspace projecting unit805, an similarity calculating unit806, a similarity combining unit807, and a face determining unit808.

The face input unit801acquires a face image by a camera (step701inFIG. 7), clips a facial region out of the image (step702inFIG. 7), and raster-scans the facial region into a vector (step703inFIG. 7).

The facial region can be determined by a positional relationship of facial feature points, such as the pupils and nostrils, as described in JP-A-9 (1997)-251534 and Osamu Yamaguchi et al (Osamu Yamaguchi, Kazuhiro Fukui, “Smartface”—A Robust Face Recognition System under Varying Facial Pose and Expression, IEICE Trans. D-II Vol. J84-D-II, No. 6, 1045-1052, 2001). Meanwhile, by successively getting face images, facial regions can be obtained at all times.

The input subspace generating unit802, after getting vectors in the predefined number (step704inFIG. 7), determines an input subspace by the principal component analysis (705inFIG. 7).

The reference subspace storing unit803is stored with reference subspaces in the number of R.

The constraint subspace storing unit804is stored with constraint subspaces in the number of M. In order to improve recognition performance, constraint subspaces are generated taking into consideration the followings.

The cause of the performance decline includes the variation in appearance due to lighting, ornaments and the like. In order to provide a resistance to lighting variation, learning patterns require face images taken by changing the lighting conditions.

Meanwhile, in order to provide a resistance to the variation due to the ornaments (glasses) worn on the face, learning patterns require face images taken by changing the ornament.

FIG. 9shows a flow of learning constraint subspaces.

At first, prepared are learning subspaces generated by learning patterns which are acquired in various lighting conditions and ornaments conditions. From those, selected are learning subspaces of the persons satisfying the defined criterion (e.g. wearing glasses) (step901inFIG. 9).

After selecting learning subspaces of a constant number of persons (step902inFIG. 9), the basic vectors of the learning subspaces are taken as an input to principal component analysis (step903inFIG. 9).

The eigenvectors, obtained as a result of the principal component analysis, are selected in ascending order of the eigenvalue to get the basic vectors of constraint subspace (step904inFIG. 9).

The subspace projecting unit805projects the input subspace and the reference subspaces of the R persons stored in the reference subspace storing unit803, onto one of the constraint subspaces stored in the constraint subspace storing unit804(step706inFIG. 7).

The procedure of projection may use a method described in JP-A-2000-30065 and Fukui et al cited on p.2.

The similarity calculating unit806calculates the similarities between the reference subspace and input subspace projected onto one constraint subspace by the mutual subspace method (step707inFIG. 7).

The subspace projecting unit805and the similarity calculating unit806exist in the number of M to carry out parallel.

Otherwise, a subspace projecting unit805and a similarity calculating unit806are repetitively used sequentially by the number M of constraint subspaces (step708inFIG. 7).

The similarity combining unit807combines the similarities by the method described in the similarity combining unit107(step709inFIG. 7). Incidentally, combining the similarities is carried out by the number R of the reference subspaces stored in the reference subspace storing unit803.

The face determining unit808determines the person of the input subspace from the combined similarity which is the highest value and greater than a preset threshold. In other cases, output is as a person not registered in the reference subspace storing unit. Output is by notification on screen or by sound with using a monitor or speaker.

The above (1) to (8) is outlined.

At first, reference subspaces in the number of R are previously prepared. These are reference subspaces G1, G2, . . . , GR made from the facial regions in the number of R persons.

Meanwhile, the constraint subspace includes constraint subspaces S1, S2, . . . , SM in the number of M, e.g. constraint subspaces that glasses are worn and constraint subspaces that lighting is applied.

Here, inputted is a facial region of the person X to make an input subspace on X.

Then, the input subspace on X and reference subspace G1are projected on to a constraint subspace S1, to thereby determine a similarity B1-1.

Next, the input subspace on X and the reference subspace G1are projected onto a constraint subspace S2, to thereby determine a similarity B2-1.

Then, a combined similarity is determined on each reference subspace. Namely, the similarities B1-1, B2-1, . . . , BM-1are combined together, to determine a combined similarity J1between the input subspace on X and the reference subspace G1. Also, determined is a combined similarity J2between the input subspace on X and the reference subspace G2. Subsequently, combined similarities J3, . . . , JR are determined in the similar manner.

Then, the reference subspace, having a combined similarity highest in the combined similarities J1, . . . , JR in the number of R and its value is greater than a preset threshold, provides the corresponding person.

(9) Modification Taking Account of Reducing the Amount of Calculation

In the case of sequentially calculating similarities by using a plurality of constraint subspaces, there is increase in calculation time. In order to reduce the amount of calculation, it is possible to narrow down the persons who are calculated similarities. There is shown inFIG. 10a flow of face image recognition taking account of reducing the amount of calculation.

At first, the input subspace and reference subspaces are projected onto one of the constraint subspaces in the number of M (step1007inFIG. 10). Second, similarities are calculated by the mutual subspace method (step1008inFIG. 10). Selected are the persons in the number of X (X<R) in descending order of the similarity (step1009inFIG. 10). R represents the number of the registered persons. Only on those persons, similarities are calculated by using the remaining constraint subspaces in the number of M-1(steps1010-1011inFIG. 10).

The face image recognition is true for the second to fourth embodiments, similarly.

Detailed Example of the Fifth Embodiment

Now, one of the learning methods of constraint subspaces is explained to be used in face image recognition, as a detailed example of the fifth embodiment. There is shown inFIG. 11a flow of this detailed example while inFIG. 12a configuration of a constraint subspace learning apparatus1200.

The constraint subspace learning apparatus1200comprises a learning subspace storing unit1201, a learning subspace selecting unit1202, a constraint subspace learning unit1203and a constraint subspace storing unit1204.

The learning subspace storing unit1201stores learning subspaces in the number of K. The learning subspaces generated from face images taken by varying lighting conditions or wearing ornaments, such as glasses.

The learning subspace selecting unit1202randomly selects a learning subspace from the learning subspace storing unit1201(step1101inFIG. 11). This is repeated until persons in the number of T (T<K) are selected (step1102inFIG. 11). If thus selected ones of the learning subspaces in number of T include two or more of those for same person, selection should be made anew.

The constraint subspace learning unit1203generates a constraint subspace by the principal component analysis from the basic vectors of selected learning subspaces (step1103inFIG. 11). The eigenvectors obtained as a result of the principal component analysis are selected in ascending order of the eigenvalue and taken as the basis vectors of constraint subspace (step1104inFIG. 11).

Until constraint subspaces in the number of M are stored to the constraint subspace storing unit1204, the learning subspace selecting unit1202and constraint subspace learning unit1203are used repeatedly (step1105inFIG. 11).

Learning constraint subspaces using a face image is true for the sixth embodiment.

Modification

Whereas face images of persons are used as input patterns in the above embodiments, fingerprints, voice, letters, DNA or the like may be used if identification of is feasible.