Multi-channel multi-model feature learning for face recognition

•We propose a new facial recognition system which learns the multi-channel and multi-model facial representations.•A new autoencoder with ADMM optimization which increases the recognition rates is designed.•The new system learns facial representations that promote to capture intra-facial-region changes more precisely.•The face recognition rates are boosted using unsupervised and hand-crafter features.•We achieve the state-of-the-art results on several facial datasets. Different modalities have been proved to carry various information. This paper aims to study how the multiple face regions/channels and multiple models (e.g., hand-crafted and unsupervised learning methods) answer to the face recognition problem. Hand crafted and deep feature learning techniques have been proposed and applied to estimate discriminative features in object recognition problems. In our Multi-Channel Multi-Model feature learning (McMmFL) system, we propose a new autoencoder (AE) optimization that integrates the alternating direction method of multipliers (ADMM). One of the advantages of our AE is dividing the energy formulation into several sub-units that can be used to paralyze/distribute the optimization tasks. Furthermore, the proposed method uses the advantage of K-means clustering and histogram of gradients (HOG) to boost the recognition rates. McMmFL outperforms the best results reported on the literature on three benchmark facial data sets that include AR, Yale, and PubFig83 with 95.04%, 98.97%, 95.85% rates, respectively.