Mapping 2D to 3D forensic facial recognition via bio-inspired active appearance model

This research done is to solve the problems faced by digital forensic analysts in identifying a suspect captured on their CCTV. Identifying the suspect through the CCTV video footage is a very challenging task for them as it involves tedious rounds of processes to match the facial information in the video footage to a set of suspect’s images. The biggest problem faced by digital forensic analysis is modeling 2D model extracted from CCTV video as the model does not provide enough information to carry out the identification process. Problems occur when a suspect in the video is not facing the camera, the image extracted is the side image of the suspect and it is difficult to make a matching with portrait image in the database. There are also many factors that contribute to the process of extracting facial information from a video to be difficult, such as low-quality video. Through 2D to 3D image model mapping, any partial face information that is incomplete can be matched more efficiently with 3D data by rotating it to matched position. The first methodology in this research is data collection; any data obtained through video recorder. Then, the video will be converted into an image. Images are used to develop the Active Appearance Model (the 2D face model is AAM) 2D and AAM 3D. AAM is used as an input for learning and testing process involving three classifiers, which are Random Forest, Support Vector Machine (SVM), and Neural Networks classifier. The experimental results show that the 3D model is more suitable for use in face recognition as the percentage of the recognition is higher compared with the 2D model.