Performance of PCA Based Semi-supervised Learning in Face Recognition Using MPEG-7 Edge Histogram Descriptor

With the development of computer technology, the growing need of large databases emphasizes utmost automation in face recognition system. Labeling a huge number of people is definitely a great overhead for such system. To reduce the burden of manual labeling of faces, in this paper, we propose a PCA based semi-supervised face recognition technique using edge histogram descriptor (EHD) features. It promotes automation of system by achieving good performance toiling at minimum wage. Moreover, we establish that EHD features are not only useful for face representation but also greatly reduce the dimensionality of the representation compared with traditional pixel value representation. Thus, the space and computation complexity decrease in further stages. PCA is then applied on the EHD features instead of raw pixel intensity values of faces which traditional methods do. Using this process, we build a PCA based classifier that can iteratively update itself after classifying unlabeled training instances. We check the performance of the system using three different similarity measures. We also test our system with different levels of noise to simulate practical environment. To evaluate the proposed method, we have used ORL, Yale, Grimace and JAFFE face databases and achieve superior performance.