Research On Palmprint Recognition

Biometrics is a kind of identification technology by using the person’s physiological characteristics or behavioral characteristics.It plays a very important role in many fields, such as public safety, finance, electronic commerce, etc. The biological characteristics which can be used for identification must satisfy such features as uniqueness, universality, stability and scalability. At present the main features for identification include face, iris, fingerprint, palmprint, gait, signature, voice, vein and so on. As a new nember of biometric family, palmprints have attracted considerable attention from many research teams due to rich, stable and unique features, such as principal lines, wrinkles, ridges, minutiae pointd, singular points and texture. Nowadays, palmprint recognition technology is still in the stage of development, and further research remains to be deepened and perfect, so it is very urgent and necessary to make further study. Under this background, this paper made a study on palmprint recognition algorithm, and the works were summarized as follows:(1)In this paper we proposed a palmprint recognition algorithm based on horizontally expanded blanket dimension and it is the first time to introduce blanket dimension to palmprint recognition. We apply fractal dimension into palmprint recognition, analyze the common fractal dimension, and expand the blanket dimension both horizontally and vertically direction. The result showed that horizontally blanket dimension was more suitable for extracting palmprint texture features. In addition, blanket dimension has multi-resolution characteristics. As the coverage increases, the upper surface and the lower surface will separate from the image, correspondingly, and the resolution will become lower. Multi-scale blanket dimension is the blanket dimension vector which was composed by different blanket dimension computed according to different surface. The algorithm only needs gray equalization processing, and then can extract the blanket dimension features. To overcome the effect of rotation, the normalized correlation is calculated using three cycle shifts which has a good robustness. If we only use one blanket dimension, namely single-scale blanket dimension, and test the algorithm on Hong Kong Polytechnic University (PolyU) database (v2) and CASIA database. The equal error rate (EER) was0.1%and0.5%respectively. If we use two or more than two blanket dimension, namely multi-scale blanket dimension, the EER can reduce to0.04%on Hong Kong Polytechnic University (PolyU) database (v2). This suggests that if multi-scale HEBDs are employed, we can obtain a smaller EER, although the computational burden is increased slightly, and the EER was quite stable, irrespective of the size of training data.(2) In this paper we first construct low quality test database such as rotation or corrosion, and fill the gap of non-ideal palmprint image recognition. We proposed palmprint recognition algorithm based on SRC、RSRC、CRC and RCRC. Sparse representation consists of two parts:residual and sparsity of coefficient In order to make the problem into a convex optimization problem,1-norm or2-norm was usually adopted in terms of measurement of residual and sparsity of coefficient. The sparse model can be decomposed into the following four models:sparse representation based classification (SRC), robust sparse representation based classification (RSRC), collaborative representation based classification (CRC) and robust collaborative representation based classification (RCRC). This paper made a detailed analysis of these four types of models and their optimization methods, and applied them to palmprint recognition respectively. Compared with SRC, although the CRC adopted the poor sparse2-norm to measure residual, but the recognition rate of CRC is more preferable and its recognition speed is greatly improved, which shows that collaborations between classes work better than coefficient sparsity. However, because2-norm was used in the residual measurement are only used on the ideal condition that there is no much difference between the train and test images. If the image to be measured covers a certain area (an injured palm) or rotates with a certain angle (Deflection acquisition palm position change or ROI was extracted from certain angle), residua has better robustness when using1-norm than2-norm. So RSRC and RCRC have a stronger robustness on the low-quality image. Especially the RCRC algorithm, when the block area is as high as50%, it also can reach a70%recognition rate.(3) Proposed a palmprint recognition algorithm based on HM-LBP and CRC. Local texture feature plays a vital role in the feature extraction, which has the following advantages. Firstly, a large number of statistical information of images was contained in the local texture information. Secondly, gray change at the local scale changes with the light can be approximated as a homogeneous and continuous change, and the influence of illumination brightness is relatively small, compared with global image. Thirdly, local texture was generally obtained through the local operator, while local operators aim at discovering the ones of strong discrimination ability and robustness, which can overcome the influence of image rotation.Local binary pattern is a common method of extracting the local texture feature, which performs well in face recognition. But the traditional LBP pattern results in the loss of some information. In addition, the percentage of information loss increased with the increase of the radius value. HM-LBP can retrieve useful information from non-uniform patterns, but it brings about the high dimensions of palmprint features. This study reduced the dimension of hierarchical multiscale LBP features with PCA. CRC can achieve high accuracy of face recognition (FR) with significantly low complexity. HM-LBP and CRC are applied together in palmprint recognition in this study. The study is conducted with the Hong Kong Polytechnic University (PolyU) database (v2) in order to test the feasibility and performance of the algorithm. The results indicate that the proposed algorithm is simple and effective with high speed and100%accuracy of recognition. We also conclude that the best PCA dimension is0.15times the number of the total training samples with extensive experiments.