Automatic door opening using gait identification for movement as gesture

• Published in: GSTF Journal of Engineering Technology (JET) Vol. 4; no. 1
• Main Authors: Indumathi, T., Pushparani, M.
• Format: Journal Article
• Language: English
• Published: Singapore Global Science and Technology Forum 21.09.2016
• Subjects: Engineering; UMLDA; Gait Biometric; SVM; Knearest neighbour Classifier; Neural
• ISSN: 2251-371X; 2251-3701; 2251-371X
• DOI: 10.7603/s40707-016-0020-7

A wide variety of organizations are using automated person identification systems to improve Customer satisfaction, operating efficiency as well as to secure critical resources. Gait identification provides a way to automatic person identification at distance in visual surveillance and monitor people without their cooperation. Controlled environments such as banks, military installations and even airports need to be able to quickly detect threats and provide differing levels of access to different user groups. Gait shows a particular way or manner of moving on foot and gait recognition is the process of identifying an individual by the manner in which they walk. Gait is less unobtrusive biometric, which offers the possibility to identify people at a distance, without any interaction or co-operation from the subject; this is the property which makes it so attractive. This paper proposed new method for gait recognition. In this thesis, first step is extraction of foreground objects i.e. human and other moving objects from input video sequences or binary silhouette of a walking person is detected from each frame and human detection and tracking will be performed. After getting binary silhouettes of human beings model based approach is used to extract the gait features of a person. This paper proposes a uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait recognition. At last neural network for matlab tool is used for training and testing purpose. We have created different model of neural network based on hidden layer, selection of training algorithm and setting the different parameter for training. And then we will test for the combination of NN+SVM, Knearest neighbour classification. Here all experiments are done on gait database and input video. Here all experiments are done on CASIA gait database. Different groups of training and testing dataset give different results. The best recognition result for our method is 96.32%.