Signature recognition

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

Multimodal biometric approach using fingerprint, face and enhanced iris features recognition

Abstract: For the last decade of year major promotions have propelled in various biometric techniques. This paper outlines a review on various biometric authentication modalities, their merits and demerits. It also discusses comparative analysis of biometric advancement in the face, fingerprint recognition, iris feature areas. To overwhelm the weakness of the Unimodal Biometric techniques, there is destitution of Multimodal biometric techniques. This paper also states a few multimodal biometric techniques and their pitfalls. This survey follows as a consequence and specified way the importance of a strong multimodal biometric technique using face, fingerprint recognition and enhanced iris features.

GEFeS: Genetic & evolutionary feature selection for periocular biometric recognition

Abstract: In this paper, we introduce the concept of genetic & evolutionary feature selection (GEFeS) for periocular biometric recognition. Our results show that GEFeS dramatically reduces the number of features needed for periocular recognition as well as increases recognition accuracy.

Novel Biorthogonal Wavelet based Iris Recognition for Robust Biometric System

Abstract: —Iris recognition has been demonstrated to be an efficient technology for doing personal identification. In this work, a method to perform iris recognition using biorthogonal wavelets is introduced. Effective use of biorthogonal wavelets using a lifting technique to encode the iris information is demonstrated. This new method minimizes built in noise of iris images using in-band thresholding in order to provide better mapping and encoding of the relevant information. Comparison of Gabor encoding, similar to the method used by Daugman and others, and biorthogonal wavelet encoding is performed. While Daugman's approach is a well-proven algorithm, the effectiveness of our algorithm is shown for the CASIA database, based on the ability to classify inter and intra class distributions, and may provide more flexibility for non-ideal images. The method was tested on the CASIA dataset of iris images with over 4,536 intra-class and 566,244 inter-class comparisons made. After calculating Hamming distances and for the selected threshold value of 0.4, FRR and FAR values were 13.6% and 0.6% using Gabor filter and 0% and 0.03% using the biorthogonal wavelets.

DVHMM: variable length text recognition error model

Abstract: This paper proposes a text recognition error model called the dual variable length output hidden Markov model (DVHMM) and gives a parameter estimation algorithm based on the EM algorithm. Although existing probabilistic error models are limited to substitution (1, 1), insertion (1, 0), and deletion (0, 1) errors, the DVHMM can handle error patterns of any pair (i, j) of lengths including substitution, insertion, and deletion.

Multimodal Biometric Authentication Using Fingerprint and Iris Recognition in Identity Management

Abstract: The majority of deployed biometric systems today use information from a single biometric technology for verification or identification. Large-scale biometric systems have to address additional demands such as larger population coverage and demographic diversity, varied deployment environment, and more demanding performance requirements. Today's single modality biometric systems are finding it difficult to meet these demands, and a solution is to integrate additional sources of information to strengthen the decision process. A multibiometric system combines information from multiple biometric traits, algorithms, sensors, and other components to make a recognition decision. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. The last 5 years have seen an exponential growth in research and commercialization activities in this area, and this trend is likely to continue. Therefore, here we propose a novel multimodal biometric authentication approach fusing iris and fingerprint traits at score-level. We principally explore the fusion of iris and fingerprint biometrics and their potential application as biometric identifiers. The individual comparison scores obtained from the iris and fingerprints are combined at score-level using a three score normalization techniques (Min-Max, Z-Score, Hyperbolic Tangent) and four score fusion approaches (Minimum Score, Maximum Score Simple Sum and User Weighting). The fused-score is utilized to classify an unknown user into the genuine or impostor.