Signature recognition

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

Pre-processing Biometric Parameters before Encoding and Decoding

Abstract: Biometric parameters acquired from human faces, voices, fingerprints, and irises are used for user authentication and access control. Because the biometric parameters are continuous and vary from one reading to the next, syndrome codes are applied to determine biometric syndrome vectors. The biometric syndrome vectors can be stored securely, while tolerating an inherent variability of biometric data. The stored biometric syndrome vector is decoded during user authentication using biometric parameters acquired at that time. The syndrome codes can also be used to encrypt and decrypt data. The biometric parameters can be pre-processed to form a binary representation, in which the binary representation has a set of predetermined statistical properties enforced imposed by a set of binary logical conditions.

Retinal recognition: Personal identification using blood vessels

Abstract: Biometrics are used for personal recognition based on some physiologic or behavioral characteristics. In this era, biometric security systems are widely used which mostly include fingerprint recognition, face recognition, iris and speech recognition etc. Retinal recognition based security systems are very rare due to retina acquisition problem but still it provides the most reliable and stable mean of biometric identification. This paper presents a four stage personal identification system using vascular pattern of human retina. In first step, it acquires and preprocesses the colored retinal image. Then blood vessels are enhanced and extracted using 2-D wavelet and adaptive thresholding respectively. In third stage, it performs feature extraction and filtration followed by vascular pattern matching in forth step. The proposed method is tested on three publicly available databases i.e DRIVE, STARE and VARIA. Experimental results show that the proposed method achieved an accuracy of 0.9485 and 0.9761 for vascular pattern extraction and personal recognition respectively.

Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks

Abstract: Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm. Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes. The expansion of networked society and increased use of some personal portable devices like tablet PCs, PDAs, mobile phones and authorization of access to sensitive data, is demanding the most reliable personal identification and authentication systems. Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used. The applications like government and legal financial transaction, bank cheques use signature as one of the personal identification system. The financial transactions and shopping using debit cards and credit cards require a bill to be confirmed by handwritten signature. But this leads to increased risk of financial loss due to attempted forgeries. This problem may be resolved by introducing automatic recognition systems which are being successfully used effectively to analyse large quantities of biometric data. Since olden days handwritten signature has been most widely used and accepted individual attributes for recognition. The design and development of signature recognition system is really big challenge because of the increased dependence of personal identification systems. Signature recognition system is divided into On-line or dynamic and off-line or static recognition. On-line recognition refers to a process where the signer uses a special pen called stylus to create his or her signature, producing the pen locations, speed and pressure, where as off-line recognition deals with signature images acquired by a scanner or a digital camera. In general, off-line signature recognition is a challenging problem, unlike the on-line signature where dynamic aspects of the signing action are captured directly as the handwriting trajectory. Contribution: In this paper, the grid and global features of signature are fused to generate final feature vector of signature. The Neural Network (NN) is used as a classifier for the verification of signatures.

Selection of points for on-line signature comparison

Abstract: Authentication based on handwritten signature is the most accepted authentication system based on biometry because it is easy to use and because the use of signature is part of our habits. In the field of authentication by on-line signature, we present a method to reduce the amount of data to be stored for pattern comparison and that needs few processing. Many systems described in literature keep the whole signature's points even if it is not recommended, even advised against it, in order to avoid forgers to obtain a signature's pattern. The proposed method for data reduction was evaluated with respect to a method of curve comparison very often used for authentication by on-line handwritten signature: dynamic time warping (DTW). After we have presented several reduction methods of signature data, we show the results obtained with each one. In order to evaluate their efficiency, the results were compared to those obtained with the whole coarse data points of the signature.

Human recognition system for outdoor videos using Hidden Markov model

Abstract: Human recognition is an essential requirement for human-centric surveillance, activity recognition, gait recognition etc. Inaccurate recognition of humans in such applications may leads to false alarm and unnecessary computation. In the proposed work a robust background modeling algorithm using fuzzy logic is used to detect foreground objects. Three distinct features are extracted from the contours of detected objects. An unique aggregated feature vector is formed using a fuzzy inference system by aggregating three feature vectors. To minimize computation in recognition using Hidden Markov model (HMM), the length of final feature vector is reduced using vector quantization. The proposed method is explained using five basic phases; background modeling and foreground object detection, features extraction, aggregated feature vector calculation, vector quantization, and recognition using Hidden Markov model.