Signature recognition

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

Off-line Signature Identification Based on DWT and Spatial Domain Features

Abstract: Hand written signature is complex behavioral biometric trait and is widely accepted for personal and document authentication. In this paper we propose Off-line Signature Identification Based on Discrete Wavelet Transform (DWT) and Spatial Domain Features (OSIDS) method. The method is tested using genuine and skilled forgery signatures. The signature is preprocessed using edge detection, filtering and morphological operation to convert into single pixel width. The global features are extracted from preprocessed signature. The DWT is applied on original signature to obtain features from four sub bands. The global features are fused with DWT features to derive final set of features. The test signature features are compared with data base signature features vector using correlation technique. It is observed that the values of FAR and EER are low in the case of proposed algorithm compare to existing algorithm. As FAR value is less, that indicates skilled forgery is successfully rejects.

Securing Biometric Template using a Hybrid Scheme

Abstract: Biometric authentication has pulled in significant attention in the course of recent years. Due to the recognition accuracy of biometric verification system, it has been used in various fields. As biometrics used in more and more applications, it is vital to protect the biometric template. Since the biometric characteristics cannot be changed. To avoid this, the biometric template protection techniques are developed. The template protection approaches could be broadly classified as transform based and cryptosystem based approaches. In this paper, we address the issue of security and privacy of the stored biometric template in the database. We propose a novel approach which combines two different approaches namely biohashing and fuzzy extractor. This approach is based on transformation of biometric features which is called as biohashing and a key generating technique called fuzzy extractor. The proposed system provides security and privacy to the biometric templates and also it provides revocable and non-invertible properties to the biometric templates. Experimentation shows the effectiveness of the proposed methods comparing with existing works.

In-Air 3D Dynamic Signature Recognition using Haptic Devices

Abstract: Signature recognition is one of the most widespread and legally accepted methodology to authenticate a person’s identity. In this work, we show how a haptic device can be used to acquire in-air 3D signatures, and provide the time-dependent position and orientation characteristics needed to effectively perform user verification. Dynamic time warping and hidden Markov models are here employed to compare samples acquired during the enrolment and verification stages. The recognition performance achieved when testing the proposed system on samples captured from 52 subjects testify the effectiveness of the proposed approach. Furthermore, a longitudinal analysis carried out on data from a subset of 21 subjects, for which two recording sessions have been taken at an average distance of four months, demonstrates that effective recognition can be performed even at long time distances from the enrolment.

Polar Transformation System for Offline Handwritten Character Recognition

Abstract: Offline handwritten recognition is an important automated process in pattern recognition and computer vision field. This paper presents an approach of polar coordinate-based handwritten recognition system involving Support Vector Machines (SVM) classification methodology to achieve high recognition performance. We provide comparison and evaluation for zoning feature extraction methods applied in Polar system. The recognition results we proposed were trained and tested by using SVM with a set of 650 handwritten character images. All the input images are segmented (isolated) handwritten characters. Compared with Cartesian based handwritten recognition system, the recognition rate is more stable and improved up to 86.63%.

Facial Expression Recognition with Discriminative Common Vector

Abstract: Extracting stable features from face images is very important for automatic recognition of facial expression. In this paper, we apply a face feature extraction approach, namely discriminative common vectors, for the recognition of the six expressions including happy, sad, angry, disgust, fear and surprise. By applying discriminative common vector, we can reduce the dimensionality of image feature and classify them in a lower dimension. Then we use HMM as our classifier to find the time series information of the feature vector projected by common vector. Experimental results on the Cohn-Kanade database demonstrate the validity and efficiency of our approach.