Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Learning Approach for Offline Signature Verification Using Vector Quantization Technique

Abstract: Signature is a behavioral trait of an individual and forms a special class of handwriting in which legible letters or words may not be exhibited. Signature Verification System (SVS) can be classified as either offline or online. [1] In this paper, we used vector quantization technique for signature verification. The data is captured at a later time by using an optical scanner to convert the image into a bit pattern. The features thus extracted are said to be static. Our system is designed using cluster based features which are modeled using vector quantization as its density matching property provides improved results compared to statistical techniques. The classification ratio achieved using Vector Quantization is 67%.

Signature verification: A study

Abstract: Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time consuming task. Consequently an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioral features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

Secure biometric template protection via randomized dynamic quantization transformation

Abstract: Fuzzy Commitment Scheme is one of the biometric encryption approaches for biometric template protection. The idea is to bind an identifier with a biometric template in binary format called difference vector during enrollment. Ideally, a difference vector is infeasible to recover either the biometric template or the identifier without any knowledge of the user's biometric data. Yet, this is only valid if the biometric template is uniformly random, but this is not the case in reality. In this paper, we propose a method known as randomized dynamic quantization transformation (RDQT) to binarize biometric data but still highly distinctive among the users and highly random. We demonstrate the implementation in the context of fingerprint biometrics. The experiment results and the security analysis in DB1 (FVC 2002) dataset suggest that the technique is feasible in practical usage.

Face recognition using discrete cosine transform plus linear discriminant analysis

Abstract: Face recognition is a biometric identification methodwhich among the other methods such as, finger printidentification, speech recognition, signature and hand writtenrecognition has assigned a special place to itself. In principle, thebiometric identification methods include a wide range of sciencessuch as machine vision, image processing, pattern recognitionneural networks and has various applications in film processing,control access networks and etc. There are several methods forrecognition and appearance based methods is one of them. One of the most important algorithms in appearance based methods islinear discriminant analysis (LDA) method. One of the drawbacksfor LDA in face recognition is the small sample size (SSS) problemso it is suggested to first reduce the dimension of the space usingmethods among which, principal component analysis (PCA) is themost popular one. In this paper we show that there exist strongermethods such as discrete cosine transform (DCT).