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Research On Theory And Algorithms Of Automatic Palmprint Recognition

Posted on:2010-06-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:J X ZhangFull Text:PDF
GTID:1118360302960635Subject:Mechanical design and theory
Abstract/Summary:PDF Full Text Request
Biometric technology provides a highly reliable and robust approach to personal verification. Palmprint recognition technology is a relatively new biometric technology but develops rapidly. Palmprint recognition technology is one of the most active and challenging research fields, and closely related to many disciplines such as Computer Vision, Pattern Recognition and Image Processing etc. Its research achievements would greatly contribute to the development of these disciplines. It is believed that automatic palmprint recognition would have a great deal of potential applications in information security, public security and law enforcement etc. Though palmprint recognition technology has made much progress, many problems still remain unsolved in practical applications. In this dissertation, the theory and algorithms of palmprint recognition are studied. The main contributions of the research work are as follows:(1) A Log-Gabor wavelet phase congruency based palmprint line feature extraction approach is presented. Line feature is the most essential and remarkable feature of palmprint image, but its effective extraction is still a difficulty due to palm-lines' speciality and complexity. In this dissertation, a frequency domain algorithm called Log-Gabor wavelet phase congruency is adopt to extract palmprint line feature, including palmprint directional phase congruency feature (PDPCF) and palmprint global phase congruency feature(PGPCF). The proposed method can not only simultaneously extract structure, strength and width information of palm-lines, but provide well feature location accuracy. Palm-lines are then detected from PGPCF image and employed to recognition palmprint images. Experiment results show the effectiveness of the proposed line feature extraction approach.(2) A palmprint region-based orientation code (PROC) scheme is proposed for palmprint line feature representation. In PROC, each PDPCF image is evenly divided into small sub-blocks. The orientation of one palmprint sub-region, which can be determined by comparison of the grey statistic results in six directions, is coded using three binary digits. PROC combines palm-lines' strength feature and orientation feature. It not only can provide better discriminant capability, but also is robust to the translation and rotation of palmprint image. Experiment results on PolyU palmprint database illuminate that the proposed PROC algorithm is effective for palmprint recognition.(3) A scatter-difference discriminant locality preserving projections (SDLPP) algorithm is put forward for palmprint image recognition. Subspace methods have been widely applied for biometric recognition for their computational convenience, good feature representation and discrimination capability. Linear discriminant analysis (LDA) and locality preserving projections (LPP) are two classic linear methods. LDA focus on the separability of data while LPP pays more attention to samples' local relationship. SDLPP combines the basic ideas of LDA and LPP, aiming at preserving local relationship of image data from the same class while making the image classes nearby in image space be far from each other in projection space. The dissertation also presents a parameter selection method for SDLPP and extends SDLPP into its nonlinear form by the kernel theory. Experiment results illuminate that SDLPP outperforms LDA and LPP on recognition accuracy and the parameter selection method is effective.(4) A mobile-based palmprint recognition algorithm is proposed, and then an embedded palmprint verification system running on Lenovo ET980 intelligent phone is developed. Firstly, the dissertation designs a novel palmprint image acquisition style and corresponding location and segmentation method, which ensures the feasibility of mobile-based palmprint recognition. Secondly, an illumination preprocessing operation is added to reduce the illumination variation influence. Finally, Gabor wavelet and subspace method are combined to extract palmprint feature. Then a genetic algorithm is employed to optimize the number and location of Gabor nodes, which can significantly improve the recognition efficiency. The proposed palmprint algorithm on Lenovo ET980 phone can achieve the recognition accuracy of EER=3.42%. The developed mobile-based palmprint verification system also satisfies the time requirement of a real-time verification system.
Keywords/Search Tags:Palmprint Recognition, Feature Extraction, Phase Congruency, Gabor Wavelet Transform, Subspace Method, Mobile-based Device
PDF Full Text Request
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