Signature recognition

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

K-Nearest Neighbor-Based for Online Baseline Identification

Abstract: A signature is the writerpsilas self representation. It contains many features; one of it is its baseline. The information contained in the baseline is known to be used as a precondition for subsequent handwriting or signature recognition algorithms and many researches regarding it has been done using off-line data. This research focuses on identifying the signature baseline by applying the concept of k-nearest neighbor. From the algorithm developed, it was found that out of 100 signatures from the database, 85% of the signature baselines were able to be identified.

Face Recognition and Detection Using Hausdorff Distance, SURF

Abstract: 2 Abstract: In the pattern recognition and computer vision field, an intriguing and a challenging problem that is widely studied is biometric based recognition by face. Biometric refers to authentication techniques that rely on measurable physical and behavioural characteristics that can be automatically verified. There are several types of biometric identification schemes namely fingerprint, hand geometry, retina, iris, signature, vein, voice, and face. Face biometric is the analysis of facial characteristics. Face Recognition is an application of biometric and it has utilizations in authentication by biometric, video surveillance, security and so forth. Face recognition system is a computer application which is capable of identifying or verifying an individual from a digital image or a video frame. One way to do this is by comparing the facial features selected from the input image and a facial image stored in database. In earlier years, several techniques for recognition by face biometric were prospected. Nevertheless, these techniques were affected from dilemma such as pose, illumination variations, increased distance between individual's face and camera can blur the image and noise was also one of the reason due to which earlier techniques were with destitute performance. In this paper face recognition and detection is done by using Hausdorff Distance with SURF and SVM. The implementation of research is on image processing toolbox under Matlab software.

Biometric Authentication using Nonparametric Methods

Abstract: The physiological and behavioral trait is employed to develop biometric authentication systems. The proposed work deals with the authentication of iris and signature based on minimum variance criteria. The iris patterns are preprocessed based on area of the connected components. The segmented image used for authentication consists of the region with large variations in the gray level values. The image region is split into quadtree components. The components with minimum variance are determined from the training samples. Hu moments are applied on the components. The summation of moment values corresponding to minimum variance components are provided as input vector to k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU database consisting of 45 subjects. The number of subjects with zero False Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance Rate [FAR] was 45. This paper addresses the computational load reduction in off-line signature verification based on minimal features using k-means, fuzzy k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and FAR of 10% was achieved using k-nn classifier. The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. The system aims to provide simple, fast and robust system using less number of features when compared to state of art works.

Fingerprint Extraction Using Minutiae Score Matching: A Review

Abstract: The purpose of this paper is to study the use of distinctive anatomical (eg, fingerprint, face, iris) and behavioral (eg, speech, gait, signature) characteristics, or traits called biometric identifiers or characteristics to automatically recognize individuals. Biometrics is becoming an required component of effective staffing solutions for biometric identification that cannot be shared or misplaced, and represent intrinsically corporeal identity of the individual . The fingerprint recognition has a very good balance of all properties. A number of biometric characteristics are used in various applications such as universality, uniqueness, performance, measurability acceptability and circumvention [2].