A Robust and Scalable Approach to Face Identification

The problem of face identification has received significant attention over the years. For a given probe face, the goal of face identification is to match this unknown face against a gallery of known people. Due to the availability of large amounts of data acquired in a variety of conditions, techniques that are both robust to uncontrolled acquisition conditions and scalable to large gallery sizes, which may need to be incrementally built, are challenges. In this work we tackle both problems. Initially, we propose a novel approach to robust face identification based on Partial Least Squares (PLS) to perform multi-channel feature weighting. Then, we extend the method to a tree-based discriminative structure aiming at reducing the time required to evaluate novel probe samples. The method is evaluated through experiments on FERET and FRGC datasets. In most of the comparisons our method outperforms stateof-art face identification techniques. Furthermore, our method presents scalability to large datasets.