Biometrics recognition is a kind of identification technology that uses the human's special physiology or behavior characteristic, it provides a kind of high reliability, good stability approach of identity verificiation. As a new member of biometric family, palmprint has drawn great attentions from many research teams from all round the world since it has rich information, stable and uniqueness features. As a result, various palmprint recognition methods have been proposed.The key issue of a successful palmprint recognition approach is how to extract discriminant features from a palmprint image. Many feature extraction methods have been proposed. Among them the subspace methods have appealing properties, such as low time-consuming, good performance on expression and separation. It can obtain good recognition performance with less feature vectors. Palmprint images used for experiments come from PolyU database. This dissertation focuses on the feature extraction technologies based on subspace methods, and studies a series of efficient palmprint recognition algorithms. In detail, the main jobs and contributions are as follows:(1) This dissertation studies palmprint image preprocessing to match without the influence of orientation. First, traditional median filter is utilized to reduce system noises effectively, and threshold segment method is applied to palmprint images for image segment. Then, the inner border tracing algorithm is employed to find the palm border, and the horizontal distance between border pixels and any point P_s of the intersection line that is formed by the wrist and bottom margin of a palmprint image is calculated. There are two local minimums in distance distribution diagram. We can find two finger-web locations by the two local minimums. Finally, the coordinate system is founded based on the two fing-web locations. Palmprint region of interest (ROI) can be orientated automatically.(2) In order to reduce the computational quantity, and extract data feature which are propitious to classification, a palmprint recognition method based on wavelet transform and partial least square (PLS) is studied. A palmprint image is decomposed to low frequency sub-images by three-level wavelet transform. Then PLS is applied to get palmprint subspace. The original palmprint image was mapped into the subspace to get feature vector for classification.(3) In palmprint and other image recognition fields, Fisher linear discriminant (FLD) method often has no answer because of small sample size problem. In small sample size cases, in order to extract classification feature with FLD and overcome the disadvantage of linearity methods which cannot effectively extract nonlinear characteristics between bixels, a novel method for palmprint recognition based on kernel principal component analysis (KPCA) and FLD is explored here. In the algorithm, after the utilization of KPCA as a pre-processing step to reduce the dimensionality of a palmprint image, the 2D image matrix is then transformed into 1D image vector. FLD is used to extract classification feature vectors for all palmprint image vectors of PolyU palmprint database. Then the cosine distances between feature vectors are calculated to match palmprints.(4) In small sample size cases, when locality preserving projection (LPP) is applied to palmprint recognition, the matrix of the eigenvalue equation is singular. The traditional solution is to utilize the principal component analysis (PCA) as a pre-processing step aiming to reduce the dimensionality of the palmprint space, then LPP is applied to extract feature. Since the projection criterion of the PCA and that of LPP are essentially different, the pre-processing step applied the PCA to reduce the dimensionality could result in the loss of some important discriminatory information. On the other hand, classical PCA reduces the dimension based on 1D vector. Palmprint image matrixes are expressed as vector, which destroys the original space structure relations between bixels. These space structure relations are not neglectable for classification. To solve the above problems, and improve convetional PCA+LPP, three methods based on image matrix, the three-level wavelet transform, image down-sample, and the mean of block segmentation, are presented to reduce palmprint space dimensionality. Then LPP is used to extract the local structure features. The cosine distances between feature vectors are calculated to match palmprints.(5) LPP is an unsupervised learning method, and it doesn't consider class information of samples applied to extract palmprint features, so the classification performance is not ideal. In order to obtain the best palmprint features in discriminant point of view, a new palmprint recognition method based on LPP and kernel direct discriminant analysis (KDDA) is explored. In small sample size image recognition, in order to resolve the singularity of the eigenvalue equation, image down-sample is first applied to reduce the palmprint dimensionality, and LPP is then used to extract the local structure features. The local features are the input of KDDA to extract classification features. Then the cosine distances between feature vectors are calculated to match palmprints.(6) Non-negative matrix factorization (NMF) has non-negative and local characteristics, and it is a new feature extraction method. NMF is unsupervised learning method, and doesn't consider class information of samples applied to extract palmprint features, so the classification effect is not ideal. In order to use class information better after the negative and local features of images are extracted, a new palmprint recognition method based on non-negative matrix factorization and general discriminant analysis (GDA) is proposed. Before extracting features, the three-level wavelet transform is utilized to palmprint images to get the low resolution sub-images. Then NMF and GDA are applied to extract non-negative and local palmprint classification features. Then the cosine distances between feature vectors are calculated to match palmprints. |