Learning individual-specific dictionaries with fused multiple features for face recognition

Recent researches emphasize more on exploring multiple features to improve classification performance. One popular scheme is to extend the sparse representation-based classification framework with various regularizations. These methods sparsely encode the query image over the training set under different constraints, and achieve very encouraging performances in various applications, especially in face recognition (FR). However, they merely make an issue on how to collaboratively encode the query, but ignore the latent relationships among the multiple features that can further improve the classification accuracy. It is reasonable to anticipate that the low-level features of facial images, such as edges and smoothed/low-frequency image, can be fused into a more compact and more discriminative representation through some relationships for better FR performances. Focusing on this, we propose a unified framework for FR to take advantage of this latent relationship and to fully make use of the fused features. Our method can realize the following tasks: (1) learning a specific dictionary for each individual that captures the most distinctive features; (2) learning a common pattern pool that provides the less-discriminative and shared patterns for all individuals, such as illuminations and poses; (3) simultaneously learning a fusion matrix to merge the features into a more discriminative and more compact representation. We perform a series of experiments on public available databases to evaluate our method, and the experimental results demonstrate the effectiveness of our proposed approach.