Forensic video solution using facial feature-based synoptic Video Footage Record
Person specific identification is an important problem in computer vision. However, forensic video analysis is the tool in surveillance applications, such as a specific person Video Footage Record can be used to help personalised monitoring. This study proposes a solution to identify the specific person very quickly through offline which will be valuable to analyse the incident/crime earlier. The main idea of this study is to reduce the enormous volume of video data by using an object-based video synopsis. After that, Viola–Jones face detection, deformable part based models are used to detect the face attributes. Subsequently, histogram of oriented gradients and oriented centre symmetric local binary pattern features are extracted. Support vector machine classifier is used to classify the weak and strong features. These strong features are used to recognise the person. The algorithm works well even in complicated situations such as expression changes, pose, illumination variations and even if the face is partially as well as fully occluded in few frames. The advantage of synoptic video helps to recognise the person who is not occluded in some other frames. Experimental results on benchmark and real time datasets demonstrate the effectiveness of the proposed algorithm.