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In an era of advanced computer technology world where innumerable services such as access to bank accounts, or access to secured data or entry to some national important organizations require authentication of genuine individual. Among all biometric personal identification systems, fingerprint recognition system is most accurate and economical technology. In this paper we have proposed fingerprint recognition system using Local Walsh Hadamard Transform (LWHT) with Phase Magnitude Histograms (PMHs) for feature extraction. Fingerprints display oriented texture-like patterns. Gabor filters have the property of capturing global and local texture information from blur or unclear images and filter bank provides the orientation features which are robust to image distortion and rotation. The LWHT algorithm is compared with other two approaches viz., Gabor Coefficients and Directional Features. The three methods are compared using FVC 2006 Finger print database images. It is found from the observation that the values of TSR, FAR and FRR have improved results compared to existing algorithm.

Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of the irises of an individual’s eyes, whose complex random patterns are unique and can be seen from some distance. Modern iris recognition algorithms can be computationally intensive, yet are designed for traditional sequential processing elements, such as a personal computer. However, a parallel processing alternative using Field Programmable Gate Array offers an opportunity to speed up iris recognition. Within the means of this project, iris template generation with directional filtering, which is a computationally expensive, yet parallel portion of a modern iris recognition algorithm, is parallelized on an FPGA system. An algorithm that is both accurate and fast in a hardware design that is small and transportable are crucial to the implementation of this tool. As part of an ongoing effort to meet these criteria, this method improves a iris recognition algorithm, namely pupil isolation. A significant speed-up of pupil isolation by implementing this portion of the algorithm on a Field Programmable Gate Array.

Biometric identification technique like offline signature verification and recognition is now a day considered as one of the important personal identification method used to identify the individual. Feature extraction is the best technique which preserves the essential information of the input image. In this paper we propose offline signature verification based on Transform domain feature such as gradient, coherence and dominant local orientation. The acquired image is resized to bring all the signatures into a uniform size. The images are thinned using morphological process. The DWT technique is applied on signature images to get LL, LH, HL and HH subbands. The directional information feature is computed from the subbands. The directional features and textural features are concatenated to form the feature vector. The Feed Forward ANN tool in MATLAB is used for classification and verification. The results of False Rejection Rate (FAR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are obtained for GPDS-960 database. A total of 360 images are used for training and testing. It is observed that the values of FRR, FAR and TSR are improved compared to the existing algorithms.