Appearance Manifold Model Based on Heteroscedastic PLDA for Video Face Recognition
Sun Weiqian · Video Engineering · 2014
For the issue existing video face recognition methods can not well learn specific covariance of local model,appearance manifold modeling based on heteroscedastic Probabilistic Linear Discriminant Analysis( PLDA) is proposed to recognize video face better. Firstly,all faces in training sets are appearance manifold modeled respectively by Gaussian distribution collection. Then,faces got from video are clustered and heteroscedastic PLDA is used to learn the clustering results so as to get characterization distribution parameters. Finally,distances between points and models are used to fusion and match all clusters from each frame of testing face and training sets,and criterion of getting highest matching scores is used to finish classification.The effectiveness and stability of proposed algorithm is verified by experiments on Honda and MoBo,experimental results show that proposed algorithm has improved recognition accuracy and decreased the computing complexity comparing with several advanced video face recognition algorithms,so it is expected to be applied into real-time video face recognition systems.