Signature recognition

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

Multi-view Human Recognition based on Face and Gait Features Detection

Abstract: In this paper, we proposed a new multi-view human recognition method based on face and gait features detection algorithm. For getting the position of moving object, we used the different of two consecutive frames. And then, base on the extracted object, the first important characteristic, walking direction, will be determined by using the contour of head and shoulder region. If this individual appears in camera with frontal direction, we will use the face features for recognition. The face detection technique is based on the combination of skin color and Haar~like feature whereas eigen-images and PCA are used in the recognition stage. In the other case, if the walking direction is frontal view, gait features will be used. To evaluate the effect of this proposed and compare with another method, we also present some simulation results which are performed in indoor and outdoor environment. Experimental result shows that the proposed algorithm has better recognition efficiency than the conventional single view recognition method.

The development of modern Biometric Recognition Technology

Abstract: Biometric recognition,or simply biometrics,refers to the automatic recognition of individuals based on their physiological and or behavioral characteristics.This paper gives a survey of the field of biometrics,such as fingerprint recognition,iris recognition,face recognition,etc,and summarizes the important research issues in biometrics.The comparison of some mentioned biometrics is given,and the direction of biometrics and multi modal biometrics is concluded.It discussed in detail the identification principle,features,the advantages and disadvantages of different method.The prospects and development of those methods are also analyzed.

Handwritten signature verification: Online verification using a fuzzy inference system

Abstract: Biometric features posses the significant advantage of being difficult to lose, forget or duplicate. Hence, a FIS-based method is used for signature verification. FIS is well suited for this task due to the similarity between an individual signatures with subtle differences between each signature sample. Signature samples are collected using a tablet PC. The individuals draw their signatures usinga pressure sensitive pen on the tablet. Eight dynamic features are extracted from the signature data. These eight features are then fuzzified for training of a FIS. The system is then used to determine whether the signature is genuine or forged. A False Acceptance Rate (FAR) of 10.67% and a False Rejection Rate (FRR) of 8.0% demonstrate the promise of this system.

2D objects recognition by graph matching

Abstract: Presents an algorithm for recognition of plane and rigid objects. The method represents the shape of these objects by valuate graphs. The author constructs a Markov model of a matching for two graphs taking into account local geometric constraints. The author finally proposes a distance between two graphs to be able to discriminate their shapes. >