Novel face recognition relevance feedback algorithm for video
Wu Yue · Journal of Xidian University · 2012
How to fully utilize both spatial and temporal information in video to overcome the difficulties existing in the video-based face recognition,such as the low resolution of face images in video,large variations of face scale,radical changes of illumination and pose as well as occasional occlusion of different parts of faces,is the key problem.In this paper,on the basis of Locality Preserving Projections(LPP),we propose a novel relevance feedback video face recognition method(RFVLPP),which can preserve more spatial and temporal information hidden in the video face sequence using clustering,and make full use of the intrinsic nonlinear structure information to extract discriminative manifold features.The experiment compares RFVLPP with other algorithms on UCSD/Honda Video Database and our own Video Database.Experimental results show that the proposed approach can outperform state-of-the-art solutions for video-based face recognition.