We present face recognition schemes based on video streams: the majority decision rule and HMM maximum likelihood (ML) decision rules. PCA type of subspace feature analysis is first applied to the face images in a video segment of a fixed number of frames. The majority decision rule is then applied to PCA recognition results in the video segment. Discrete HMM (DHMM) is also applied to the single-frame recognition sequences. Continuous density HMM (CDHMM) is applied directly to the sequence of PCA feature vectors for ML decision on the video segment in a delayed decision manner. Experimental results are compared between these three schemes in terms of the number of states and Gaussian mixtures of the HMMs. CDHMM-based decision rule achieved a 99% correct recognition rate in average. A geometric interpretation of ML in the feature subspace well explains the observed performances.
['Kohsia S. Huang', 'Mohan M. Trivedi']
Streaming face recognition using multicamera video arrays