Signature recognition

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

Comparative Analysis of Global Feature Extraction Methods for Off-line Signature Recognition

Abstract: this paper, a method is proposed for feature extraction of offline signature recognition system. The proposed method is based on global features to identify forgeries and also median filter is introduces for noise reduction. The Proposed feature extraction method is compared with Discrete Radon Transform (DRT). Both the feature extraction method extracts one dimensional global features and the alignment between features is performed by Dynamic Time Warping (DTW). When being trained using 6 genuine signatures of each person and 250 forgeries taken from our database, the proposed method obtained an equal error rate (EER) of 8.40%. The false acceptance rate (FAR) for proposed method was also kept as low as 8.80%. Keywordsfilter, Global features, Discrete Radon transform, Dynamic time warping.

Online signature recognition using Kekre's vector quantization algorithms KMCG & KFCG

Abstract: Dynamic Signature Recognition is one of the highly accurate biometric traits. We capture live signature of the person hence it is possible to analyze dynamic characteristics of signature for matching purpose. The signature captured by digitizer gives information about dynamic nature of signature and pressure applied while signing. Dynamic parameters such as pressure, X, Y, Z- co-ordinates, Azimuth & Altitude are captured. These signature points are vector in n-dimensional vector space. In this paper we have proposed clustering of these points in vector space to form feature vector is proposed for online signature recognition. For clustering & codebook generation kekre's Vector Quantization Algorithms such as KFCG, KMCG are used with variations. The proposed technique gives up to 97% accuracy.

A new linear appearance-based method in face recognition

Abstract: Human identification recognition has attracted scientists for many years. During these years, and due to increases in terrorism, the need for such systems has increased much more. The most important biometric systems that have been used during these years are fingerprint recognition, speech recognition, iris, retina, and hand geometry, and face recognition. For comparing biometric systems, four features have been considered: intrusiveness, accuracy, cost, and effort. The investigation has shown that among the other biometric systems, face recognition is the best one [1]. A face recognition system has three parts—face localization, feature extraction, and classification. In face localization, part of the background and other parts of the image that may influence the recognition process is removed from the image. For this reason, the face is found in the image and the system just works on this part of the image. For simplicity, we ignore this part of the system. In the feature extraction part, the unique patterns of the face will be extracted from the image, and in the classification part these patterns will be placed in the class in which they belong. Each class shows a person’s identity. The process of extracting the most discriminating features is very important in every face recognition system. In this chapter, we introduce fractional multiple exemplar discriminant analysis, which is a variation of a linear discriminant analysis algorithm. The results show that the proposed method, combined with RBF neural networks, has better results in comparison to other methods.