iosr-JCE   Volume-10 ~ Issue-1

Abstract: A phenomenal growth in the number of credit card transactions, especially for on-line purchases, has also led to a substantial rise in fraudulent activities. Credit card fraudulent transactions are very easy to conduct, while very difficult to recover, compared to the fraud cases in hard-products transactions. In real life, fraudulent transactions could be interspersed with genuine transactions and simple pattern matching techniques are not often sufficient to detect the fraudulent transactions efficiently. Moreover, surrogate representations of identity can be easily forgotten, lost, guessed, stolen, or shared. Further it can be hacked through malicious websites or prone to security breaches. Implementation of efficient fraud detection systems has thus become imperative for all credit card companies in order to minimize their losses. In this paper, we propose a IRPV (Iris Recognition and Palm Vein) recognition technology which will help add even more security to existing biometric devices that may be susceptible to fraud. The techniques used are Palm vein technology, along with iris recognition. It is difficult to crack, because each person's vein pattern is unique. Thus, biometric systems impart higher levels of security when appropriately integrated into applications requiring user authentication. The experimental results show that the detection rate would prove 99.995% compared to traditional methods.

Index Terms - Credit Card Fraud, Iris Recognition, Palm Vein technology.

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Abstract: Video face recognition is a widely used method in which security is essential that recognizes the human faces from subjected videos. Unlike traditional methods, recent recognition methods consider practical constraints such as pose and illumination variations on the facial images. Our previous work also considers such constraints in which face recognition was performed on videos that were highly subjected pose and illumination variations. The method asserted good performance however; it suffers due to high computational cost. This work overcomes such drawback by proposing a simple face recognition technique in which a cost efficient Active Appearance Model (AAM) and lazy classification are deployed. The deployed AAM avoids nonlinear programming, which is the cornerstone for increased computational cost. Experimental results prove that the proposed method is better than the conventional technique in terms of recognition measures and computational cost.

Keywords — Active Appearance Model (AAM), lazy classification, shape, model, appearance, recognition, computation.

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[10] SushmaJaiswal, Sarita Singh Bhadauria, Rakesh Singh Jadon and Tarun Kumar Divakar, "Brief description of image based 3D face recognition methods", 3D Research, Vol. 1, No. 4, pp. 1-15, 2010 .

Abstract: DNA sequencing generates a large number of reads of lengths varying from 100bp to 1000bp, when sequenced using different methods of sequencing. These reads are further assembled to form contigs which are useful in annotation. The library generation using different amplification technique is involved in DNA sequencing process, which generates several identical reads, which are redundant, resulting in degraded quality of sequencing, besides also causing longer time for assembly. Existing computationally complex algorithms use string processing. The paper discusses the signal processing approach with application of Wavelet Transforms, designed to find exact and near exact identical reads. The string processing approach for pattern matching in search of similar patterns is computationally very expensive because the order of complexity of String comparisions is exponential in nature. Whereas Wavelet Transforms translates the sequence in co-efficients which are half of the length of the original sequence. On applying Wavelet Transforms repeatedly on the sequence, the sequence get transformed to half the length of the sequence used for transformations. Thus the order of complexity reduces to O(log n), which is much efficient compared to string processing.

Keywords – Haar wavelets, identical reads, pattern recognition, signal processing, wavelets,.

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