FACE RECOGNITION – MACHINE LEARNING HIGHWAY TO EFFICIENCY

Face recognition describes the automated computer managed human face recognition on digital media, image or video. Even though started in the 1960’s, the hype surrounding face recognition nowadays is more increasing, partially because of security applications it could provide and reduce possible security oriented threats. This paper is going to show directions of recent research in this fi eld, with an accent on used algorithms, data sets for higher effi ciency, and an overview of predominantly used sensors. One of the concept that is introduced lately in the fi eld are various types of machine learning as means for creating more effi cient face recognition systems. By presenting diff erent approaches, possible directions are given for future research

Mutual component analysis for heterogeneous face recognition

Heterogeneous face recognition, also known as cross-modality face recognition or intermodality face recognition, refers to matching two face images from alternative image modalities. Since face images from different image modalities of the same person are associated with the same face object, there should be mutual components that reflect those intrinsic face characteristics that are invariant to the image modalities. Motivated by this rationality, we propose a novel approach called Mutual Component Analysis (MCA) to infer the mutual components for robust heterogeneous face recognition. In the MCA approach, a generative model is first proposed to model the process of generating face images in different modalities, and then an Expectation Maximization (EM) algorithm is designed to iteratively learn the model parameters. The learned generative model is able to infer the mutual components (which we call the hidden factor, where hidden means the factor is unreachable and invisible, and can only be inferred from observations) that are associated with the person's identity, thus enabling fast and effective matching for cross-modality face recognition. To enhance recognition performance, we propose an MCA-based multiclassifier framework using multiple local features. Experimental results show that our new approach significantly outperforms the state-of-the-art results on two typical application scenarios: sketch-to-photo and infrared-to-visible face recognition