Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

The Transition of World Cashew Industry and the Challenges to India

Cashew is an important commodity traded across the continents and the world cashew industry is the livelihood of more than three million people worldwide, the majority of whom are womenfolk from the socially and economically backward community of the developing nations. Cashew tree was originally planted to prevent soil erosion and it was during the beginning of the 19th century that cashew kernels attained the status of a food item. Further, the cashew kernels attained the status of an international commodity with India exporting its first consignment of cashew kernels to U.S.A. in 1920. India was the first country to hit the world market with cashew as a commodity and it was she who pioneered cashew processing as an industry. For decades together India was enjoying a monopoly in the world cashew industry in the fields of raw nut production (cultivation), processing and the market share in the international trade. The liberalisation of international trade has brought in a big transition in the world of cashew. India started to benefit from the trade policy, that improved her supply positions of raw nuts from other producing countries, accelerated her growth in processing of raw nuts and exports of cashew kernels. On the other side, her domestic consumption started growing up that by the beginning of the new century, she emerged out as the world’s largest consumer of cashew kernels as well.