A Smart Security System Using Multimodal Features from Videos

Abstract

Multi-modal biometric authentication system uses more than one biometric feature and the use of multi-modal biometrics improves security by making the system invulnerable to spoofing attacks. The proposed system uses face and gait biometrics for authentication and identification. The authentication is done in an unobtrusive manner without the knowledge and co-ordination of the user with the help of surveillance cameras. The videos are captured from two surveillance cameras, placed at fronto-parallel and fronto-normal views are given as input to the system. The gait system uses the video from fronto-parallel view and uses a model free approach to extract a spatio temporal motion summary of the gait cycle. The gait features has been compared by calculating the Euclidean distance between them. The face system uses the video from fronto normal view and uses an appearance based approach to extract features from the face of the user. The face features has been compared by calculating Chi-Square dissimilarity between them. The score level fusion is performed to provide an enhanced security system. A threshold value is kept and it is compared with the scores to authenticate the person. The Minimum Distance Classifier is used to identify the person by fusing the multimodal features.