Signature recognition

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

Comparative Study on Secure Biometrics for Human Identification

Abstract: paper, a comparative study of biometric security using various techniques is presented. This paper presents the authentication of information using fingerprint and face biometric technique. A detailed comparison between finger- print biometric and face biometric & their pros and cons while using for the devices is presented. Face biometric identification is more beneficial than fingerprint biometric identification. Face recognition provides more authentication and verification for identification in biometric.

Efficient Gesture Recognition based on Human Motion Detection

Abstract: Gait recognition is the term used for detection of Human based on the features. The Feature extraction and Feature Mapping is the main aspect to recognize the Gestures from the Database of features. Recognition of any individual is a task to identify people. Human recognition methods such as face, fingerprints, and iris generally require a cooperative subject, physical contact or close proximity. These methods are not able to recognize an individual at a distance therefore recognition using gait is relatively new biometric technique without these disadvantages. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. This project aims to recognize an individual using his gait features. However the majority of current approaches are model free which is simple and fast but we will use model based approach for feature extraction and for matching of parameters with database sequences. After matching of Features, the Images have been identified and show the dataset from it matched. The Results are accurate and shows efficiency.

Biometric authentication by offline signature images using hidden markov model

Abstract: During information technology era, where fast retrieving information is obtained using computer networking and applications, such as banking systems and border security. Offline Signature verification and validation is the process of identifying the given signatures and verifying those signatures to differentiate the original from the forged ones, by using some mathematical procedures, generated by some pattern recognition algorithm. Mostly passwords and PIN codes are very easily to be forgotten. But unlike those passwords and PIN codes, Signatures are very hard to be forgotten or even simulated by others. For that reason a system of authenticating the signature has been extensively used by the people as a secured way of identification. This paper presents an approach to find a method for signature authentication system by using Hidden Markov model. A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analyses of hidden Markov models seek to recover the sequence of states from the observed data. A publicly available database GPDS with Hidden Markov Model (HMM) as a Classifier is an idea of this paper to implement authentication of Handwritten

Research on the multi-criteria combination in automatic recognition of marking points

Abstract: Objective: In the field of computer vision, the technology for the automatic recognition of coded pattern plays an important basic role in the camera calibration process of intrinsic and extrinsic parameters, the binocular image matching process and the three-dimensional reconstruction process. Therefore, in the measurement processing, the successive rate for the automatic recognition of coded pattern must be guaranteed. Method: According to analyzing the geometric information of the coded pattern (the mixed type) and basing on the existing recognition method, a new automatic recognition method is proposed, which is the effective method to solve the multi-points recognition in single image by taking the multi-feature information of the coded pattern as the recognition criteria. Result: Both the new recognition method and the old recognition method are used in identifying the one hundred coded pattern which have been actually collected. The experimental result shows that, not only the new recognition method can achieve accurate identification of coded pattern with the recognition accuracy rate of 100%, but also its processing speed is 2.38 times faster than that in the old recognition method. Conclusion: It is obvious that there are many advantages in the new automatic recognition method, including the high effective recognition, the faster executive speed and independent on the auxiliary decoding process information. The new recognition method of multi-criteria combination can provide a strong guarantee for the realization of every aspect in the work of photogrammetry.