CN102368334A - Multimode latent semantic analysis processing method based on elder user - Google Patents

Abstract

The invention relates to the biometric identification technology field and especially relates to a multimode latent semantic analysis processing method based on an elder user. The method comprises the following steps: a. image matrix construction of low-level characteristics; b. a parallel two-dimension nonnegative matrix decomposition algorithm; c. fuzzy C mean clustering. In the invention, based on a multimode latent semantic analysis processing method based on the elder user, a multimode latent semantic analysis algorithm is used to process the extracted characteristics from three aspects: low-level characteristics- the image matrix construction, two-dimension matrix decomposition and the clustering algorithm. By using the method, acquisition quality is good and reliability is strong. Actual requirements of different occasions can be satisfied.

Description

Based on aged user's multimode implicit expression semantic analysis disposal route

Technical field

The present invention relates to technical field of biometric identification, especially based on aged user's multimode implicit expression semantic analysis disposal route.

Background technology

At present; China more than 60 years old population reach 1.8 hundred million people, account for total population 13.8%, weigh by international standard; China has got into the society of the aged; Along with country accelerates to set up and improve the social security system that covers urban and rural residents energetically, like the golden granting of social old-age insurance, supplementary pension, medical insurance etc., aged user will become the main colony of Future Society public service; Exist deception, false claiming phenomenon to become social now question of common concern in the distribution process such as social old-age insurance gold, supplementary pension, informationization, digitizing, network technology provide help for solving aged authenticating user identification quagmire.At present, biometrics identification technology, long-distance video authentication are falsely claimed as one's own aged user's in the phenomenon identity by successful Application to examining the social pension gold.

Biometrics identification technology carries out identification and checking through utilizing intrinsic physiological characteristic of human body and behavior act.According to the kind and the number that use biological characteristic; Living things feature recognition can be divided into single living things feature recognition and multi-biological characteristic identification; As using the widest single bio-identification identity identifying technology, the problem of authentication had received extensive concern when fingerprint recognition was put at the aged user's social pension golden hair of solution.As far back as 1901, Britain began employing fingerprint and has discerned and avoid the railway worker to falsely claim as one's own, lead more salary.At present, associated companies such as IBM, Microsoft, HP, Compaq, Changchun letter reach, are just waiting in the Hangzhou service field that entered society of the product of company." the payment pension fingerprint identity validation systems technology standard (trying) " of the social insurance career management center issue of China Ministry of Labour and Social Security also will issue as the social public service standard based on the fingerprint identification method of minutiae point (minutiae); But; Concerning aged user; Owing to have experienced all sorts of hardships, fuzzy finger is very common, and traditional fingerprint recognition system based on minutiae point tends to cause system's misclassification rate to increase because the extraction minutiae point is undesirable even authentication was lost efficacy.In addition; Recognition technology based on single biological characteristic exists not ubiquity: some biological characteristic disappearance (like severed digit), damage (like impaired finger), pathology (like cataract) or collection apparatus second-rate (changing like people's face light) all can cause robustness, the poor reliability of recognition system; A little less than the anti-duplicity, be difficult to satisfy the actual requirement of different occasions.

Image implicit expression semantic feature (Image Latent Semantic Features; ILSF) obtain by low-level image feature-image array; Have the information abundanter more than traditional image, semantic, but relative and low-level image feature, and these characteristics have stronger expression and classification capacity.Therefore, the characteristic of utilizing TLSA to extract can be used as the characteristic of a kind of " uniqueness ", and is proved to be and can be used in the biometric identity field of authentication.Simultaneously, compare traditional low-level image feature, owing to be used for describing image indirectly; Image implicit expression semantic feature is not very high for the quality requirements of images acquired; Can better overcome the influence that some unfavorable factor is brought, fuzzy such as the image streakline of fingerprint, and the influence of human face light variation.

Summary of the invention

The technical matters that the present invention will solve is: in order to overcome the problem that exists in above-mentioned, provide a kind of to extract that characteristic handles based on aged user's multimode implicit expression semantic analysis disposal route.

The technical solution adopted for the present invention to solve the technical problems is: a kind of based on aged user's multimode implicit expression semantic analysis disposal route, its concrete steps are following:

A. the image array of low-level image feature makes up: adopt multiple low-level image feature, make up the image array of each user's low-level image feature;

B. parallel two-dimentional nonnegative matrix decomposition algorithm: the image array to low-level image feature carries out the diagonalization processing earlier; Again diagonalizable matrix being carried out the row matrix direction decomposes; And then former diagonalizable matrix is carried out transpose process obtain column direction information, the basis matrix that obtains is carried out basis matrix orthogonalization;

C. fuzzy C-means clustering: utilize the fuzzy C-means clustering method in the programming tool case to carry out cluster.

Beneficial effect based on aged user's multimode implicit expression semantic analysis disposal route of the present invention is: utilize multimode implicit expression semantic analysis algorithm to decompose and clustering algorithm three aspects from low-level image feature-image array structure, two-dimensional matrix; Can handle the characteristic of extracting; Good and the good reliability of the method acquisition quality can satisfy the actual requirement of different occasions.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the present invention is further specified.

Fig. 1 is the synoptic diagram of two-dimensional matrix diagonalization of the present invention (a) row combination (b) row combination;

Fig. 2 is a FCM cluster synoptic diagram of the present invention.

Embodiment

Combine accompanying drawing that the present invention is done further detailed explanation now.These accompanying drawings are the synoptic diagram of simplification, basic structure of the present invention only is described in a schematic way, so it only show the formation relevant with the present invention.

As depicted in figs. 1 and 2 based on aged user's multimode implicit expression semantic analysis disposal route, its concrete steps are following:

A. the image array of low-level image feature makes up: adopt multiple low-level image feature, make up low-level image feature-image array (q biological characteristic merges altogether) of each user, its concrete steps are following:

Step 1: the ROI image unification of each biological characteristic is blocked into p size be the little image of n * n, q the individual local little image of the common p * q of biometric image;

Step 2: each local little image is comprised analyses such as invariant moment features (hu invariant moments and zernike orthogonally-persistent square), Garbor filter characteristic, direction equalization characteristic, half-tone information entropy characteristic respectively, and with the column vector of these characteristics as low-level image feature-image array of each user;

Step 3: with the row vector of the local little image behind each piecemeal (q biological characteristic) as low-level image feature-image array of each user; Add up the probability of last each low-level image feature of obtaining of step to its appearance; Make up each user's low-level image feature and the characteristic-image array between the image, its size is p * q;

B. parallel two-dimentional nonnegative matrix decomposition algorithm: at first; Low-level image feature-image array is carried out diagonalization to be handled; Then, after diagonalizable matrix being carried out the decomposition of row matrix direction, directly former diagonalizable matrix is carried out transpose process and obtain column direction information; At last, the basis matrix that obtains is carried out basis matrix orthogonalization: its key step as shown in Figure 2 is following:

Step 1: diagonalization of matrix: with size is that I is used in m the low-level image feature-image array set of p * q _{P * q}=[S ₁, S ₂..., S _m] represent S _nRepresent low-level image feature-image array of each user, m is a number of users.1), so with the capable combination of matrix, and adopts the mode row combination image of Fig. 2 (a), and obtain diagonalizable matrix A from image array if length p is not more than width q _n(zone shown in the shade).2) if length p greater than width q, is listed as combination with matrix so, and the mode row combination image of employing Fig. 2 (b), and the image array after combination obtains diagonalizable matrix A _n(zone shown in the shade).And the big young pathbreaker who obtains diagonalizable matrix is the same with original matrix, still is p * q;

Step 2: the image array line direction decomposes: with size is that m the diagonalizable matrix of p * q gathered and used X _{P * q}=[A ₁, A ₂..., A _m] represent A _nRepresent the low-level image feature-image array after each user's diagonalization, m is a number of users, and matrix H is long-pending for the matrix L of p * d and size are d * q at first to utilize 1D-NMF to be decomposed into size, makes: X _{P * q}≈ L _{P * d}H _{D * q}Here d is with reference to dimension, and L is that matrix X decomposes the basis matrix that obtains in image row direction, and H is a matrix of coefficients;

Step 3: the image array column direction decomposes: with size is that m the diagonalizable matrix of q * p gathered and used Y _{Q * p}=[B ₁, B ₂..., B _m] represent, wherein

Be that former diagonalizable matrix is carried out transpose process.Similar above-mentioned algorithm utilizes 1D-NMF to find size nonnegative matrix R and nonnegative matrix H that size is r * p for q * r, makes Y _{Q * p}≈ R _{Q * r}H _{R * p}Here, r is with reference to dimension, and R is the basis matrix that the decomposition of matrix Y on image column direction obtains, and H is a matrix of coefficients;

Step 4: matrix X and Y constitute according to the former figure matrix of training sample and transposition figure matrix thereof, therefore can carry out simultaneously their decomposition.And, to diagonalization low-level image feature-image array A of any user _n, its coefficient C on the row and column basis matrix _n=L ^TA _nR, size is d * r, obviously, vectorial dimension reduces greatly.Utilize the basic L of row and Lie Ji R reconstruct low-level image feature-image array to be expressed as: A _n≈ LC _nR ^T, n=1,2 ... m, then two-dimentional basis matrix are E=L R ^T

Step 5: basis matrix orthogonalization: the basis matrix E=L R that parallel 2D-NMF method is obtained ^TIn L and R matrix orthogonalization: L '=orth (L) and R '=orth (R) respectively.Two-dimentional basis matrix E ' after the orthogonalization=L ' (R ') like this ^TConstituted original image matrix A _nAn implicit expression semantic space, a semanteme in the corresponding subspace of every column vector.Project image onto in this semantic space, promptly obtain coefficient C by the semantic feature combination _nRepresented image implicit expression semantic feature;

C. fuzzy C-means clustering: utilize the fuzzy C-means clustering method in the programming tool case to carry out cluster.

With above-mentioned foundation desirable embodiment of the present invention is enlightenment, and through above-mentioned description, the related work personnel can carry out various change and modification fully in the scope that does not depart from this invention technological thought.The technical scope of this invention is not limited to the content on the instructions, must confirm its technical scope according to the claim scope.

Claims (1)

1. one kind based on aged user's multimode implicit expression semantic analysis disposal route, and it is characterized in that: its concrete steps are following:

A. the image array of low-level image feature makes up: adopt multiple low-level image feature, make up the image array of each user's low-level image feature;

B. parallel two-dimentional nonnegative matrix decomposition algorithm: the image array to low-level image feature carries out the diagonalization processing earlier; Again diagonalizable matrix being carried out the row matrix direction decomposes; And then former diagonalizable matrix is carried out transpose process obtain column direction information, the basis matrix that obtains is carried out basis matrix orthogonalization;

C. fuzzy C-means clustering: utilize the fuzzy C-means clustering method in the programming tool case to carry out cluster.

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