Recognising Partially Occluded Faces From a Video Sequence
Extensive research on video based face recognition has been carried out by researchers in the recent years as security is a major concern in today’s world. Performing a comparison of face recognition with a still face and upon a video, video is given more importance as it can give more information to the user compared with still face image. Even though video can provide widespread information of a face, there still exist problems with respect to video like, occlusion and pose variation. From past several years there are extensive research going on in recognising faces from video, recognising partially occluded faces remains a challenging task. There has been lot of study going on in coming up with a better recognition rate with respect to occlusion. In this paper we propose a method to recognise face from video. Adaboost algorithm is used to detect faces from each frame and if there are occluded faces they are reconstructed using in painting and texture synthesis. On the pre-processed face image that is for all the faces that are stored in the dataset with occlusion. The features are extracted using Discrete Cosine Transform. Finally matching is done against the input face image. The algorithm is tested against You tube dataset and it is found that it gives better recognition rate compared to existing algorithms.
Vijayalakshmi A , Pethuru Raj