Signature recognition

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

Dynamic biometric person authentication using pen signature trajectories

Abstract: Authentication of individuals is rapidly becoming an important issue. The authors have previously proposed a pen-input on-line signature verification algorithm incorporating pen-position, pen-pressure and pen-inclination trajectories. This paper proposes an algorithm with three new features: (i) Incorporation of pen-velocity trajectories, (ii) A new distance measure between two signatures, and (iii) A new efficient algorithm for computing the distance measure. Preliminary experimental result looks encouraging.

Face Recognition Based on Unit-Linking PCNN Time Signature

Abstract: In this paper, a novel method is proposed for face recognition based on Pulse Coupled Neural network (PCNN) time signature. In this approach, a probe face is first extracted PCNN time signature as the recognition features, which a two-dimensional image is projected to a low one-dimensional feature space and then is classified based on the known samples. An extensive experimental investigation is conducted using AT&T face database covering face recognition under controlled/ideal conditions, different illumination conditions and different facial expressions. The recognition ratio is 83.75% when only PCNN time signature, including 5 features is used as the recognition features and three exemplar images per person are available. The recognition ratio is 94.50% when the PCNN time signature is integrated with PCA feature based on Euclidean distance. The recognition ratio is 98.50% when the PCNN time signature is integrated with ICA feature based on Support Vector Machine (SVM). The simulation result demonstrates that proposed recognition method is robust due to the translation, rotation and scale invariance of PCNN time signature, insensitive to illumination and facial expressions changes as well as fast due to the low feature dimensions.

Neural network and optical correlators for infrared imaging based face recognition

Abstract: This paper deals with the problem of thermovision application for biometric people recognition. The main goal of research was the automatization of recognition process and proving, whether the infrared imaging can be exploited for realization of the biometric system. First, the data base composed of 270 images made in different conditions, was built. Next, the multilayer perception and two optical correlators such as joint transform correlator and 4f correlator, were applied. Each system was designed for realizing two various tasks: verification and identification. Finally, the systems were tested and evaluated according to the Face Recognition Vendor Tests (FRVT). Achieved results proved that temperature distribution on the human face is a biometric trait and people can be efficiently recognized with usage of infrared imagery.

An Efficient Fuzzy Identity-Based Signature Scheme without Bilinear Pairings

Abstract: Biometric-based signature is an emerging cryptographic primitive that allows a user with a biometric identity to produce a signature that can be verified successfully using another biometric identity if both biometric identities are within a predefined distance metric. Fuzzy identity-based signature is of particular value for biometric authentication, where biometric identifiers such as fingerprints, iris and voice are used in human identification. Unfortunately, constructions of fuzzy identity-based signature scheme so far are based on bilinear pairings that need costly operations. In this paper, we propose a new fuzzy identity-based signature scheme that does not depend on bilinear pairings. With both the running time and the size of the signature being saved greatly, our scheme is more practical than the previous related schemes for practical application. The proposed fuzzy identity-based signature scheme is proved to be existential unforgeability against adaptively chosen message attack under the standard discrete logarithm assumption in the selective-set model.

Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation

Abstract: Time and again hidden Markov models have been demonstrated to be highly effective in one-dimensional pattern recognition and classification problems such as speech recognition. A great deal of attention is now focussed on 2-D and possibly 3-D applications arising from problems encountered in computer vision in domains such as gesture, face, and handwriting recognition. Despite their widespread usage and numerous successful applications, there are few analytical results which can explain their remarkably good performance and guide researchers in selecting topologies and parameters to improve classification performance.