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Offline signature verification system finds several applications in monitory transaction systems like banks. However one of the major challenges in this direction is the capability of the system to detect skilled and unskilled forgery. Many cases of bank check forgeries have been reported. Most of the offline signature verification system adopts recognition based technique where the system classifies a given signature sample as one of the samples from the database. However detection of a forgery in a given sample is challenging as the input sample looks similar to one of the samples in the database. In this paper we propose an innovative approach for offline signature verification with polar feature descriptor for signature that contains Radon Transform and Zernike Moments. Verification is performed using Multiclass Support Vector Machine. Once a signature is verified as being of a registered class, PLS Regression is applied on the sample against all samples in the database of the verified user to obtain regression score. Log Likelihood of the sample against all sample of the user is calculated using Hidden Markov Model. Authenticity of the classification is justified if the regression score and Log Likelihood distance deviation is less than 5%. Results show that the system verifies signature with an accuracy of 98% with false acceptance rate of .8%. Proposed system also detects skilled forgery with an accuracy of 71% and Random forgery with an accuracy of 76%.

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.

The potential use of RAPD markers in identifying genuine intergeneric hybrids of Saccharum was examined in 12 hybrids of Saccharum with Erianthus spp., Sorghum bicolor and Zea mays. In the three groups of hybrids examined, RAPD markers could precisely identify the true hybrids of Saccharum officinarum × Sorghum bicolor and Saccharum officinarum × Zea mays. Though Erianthus had a very distinct RAPD profile and as many as 107 Erianthus-specific bands were identified, a decisive profile that can identify all the hybrids could not be obtained. The strong molecular differentiation between S.officinarum and Erianthus was not adequately resolved in the RAPD profiles of their hybrid progenies which suggest that more refined molecular techniques will be required for the characterization of Saccharum–Erianthus hybrids.

The phylogenetic relationship of sugarcane (Saccharum officinarum) with three other related genera viz.,Erianthus, Zea andSorghum and their 12 intergeneric hybrids was studied using 250 RAPD markers.S.officinarum andErianthus, though belonging to the same sub tribe, showed strong molecular differentiation between them. The phenogram based on these markers showed thatErianthus andSorghum are almost equidistantly placed fromSaccharum officinarum while maize was found to be the most outlying genus in relation toS.officinarum. The 12 hybrids of sugarcane (S. officinarum) withErianthus spp.,Sorghum bicolor andZea mays were found to be closer to theirSaccharum parent than to the respective related genera. This is possibly because of the higher chromosome contribution ofSaccharum compared to the related genera in the hybrids.

Automatic offline signature verification and recognition is becoming essential in personal authentication. In this paper, we propose a transform domain offline signature verification system based on contourlet transform, directional features and Hidden Markov Model (HMM) as classifier. The signature image is preprocessed for noise removal and a two level contourlet transform is applied to get feature vector. The textural features are computed and concatenated with coefficients of contourlet transform to form the final feature vector. HTK tool with HMM classifier is used for classification. The parameters of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are calculated for GPDS-960 database. It is found that the parameters of FRR and FAR are improved compared to the existing algorithms.

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.