Uncontrolled environments have often required face recognition systems to identify faces appearing in poses that are different from those of the enrolled samples. To address this problem, probabilistic latent variable models have been used to perform face recognition across poses. Although these models have demonstrated outstanding performance, it is not clear whether richer parameters always lead to performance improvement. This work investigates this issue by comparing performance of three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets have shown that fusion of multiple classifiers improves face recognition across poses, given that the individual classifiers have similar performance. This proves that different probabilistic latent variable models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion across multiple images has also been shown to produce better perfomance than recogition using single still image.
face recognition, pose, classifier fusion, video, probabilistic latent variable