Biometric identification method is used to assess the characteristics of human behavior by identifying their different parameters. Gait recognition is an active biometric research topic which has many security and surveillance applications, and also can help in early diagnosis of different medical conditions such as Parkinson disease. It has been concluded from Psychological studies that people have slight but substantial capability to distinguish individuals by their gait characteristics. There are different techniques to perform gait recognitioii and can be achieved by analyzing data from either imagery or radar sensors. This particular research project however will involve correct identification of a person from person's gait by using images/video taken at different distances, angle of views and walking speeds of the person. CAS1A Gait Recognition Dataset used in this project contains gait energy images. These images are extracted from images frame sequence of walking subject with camera positioned relative to subject, with increments of 18 degrees. Lower part of GE1 is used in feature extraction, as it has most dynamic information. Gait signatures of a person created from gait energy images will be used to train artificial neural networks model to correctly classify the subject. Two Back propagation algorithms arc compared in terms of performance. Cross-entropy and ROC curves arc used as performance criteria for both training algorithms. Our system performs very well in terms of minimization of cross-entropy and classification rate.
