This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON CYBERNETICS 1 Learning Multiscale Active Facial Patches for Expression Analysis

Abstract—In this paper, we present a new idea to analyze facial expression by exploring some common and specific infor-mation among different expressions. Inspired by the observation that only a few facial parts are active in expression disclo-sure (e.g., around mouth, eye), we try to discover the common and specific patches which are important to discriminate all the expressions and only a particular expression, respectively. A two-stage multitask sparse learning (MTSL) framework is pro-posed to efficiently locate those discriminative patches. In the first stage MTSL, expression recognition tasks are combined to located common patches. Each of the tasks aims to find dominant patches for each expression. Secondly, two related tasks, facial expression recognition and face verification tasks, are coupled to learn specific facial patches for individual expression. The two-stage patch learning is performed on patches sampled by multiscale strategy. Extensive experiments validate the existence and significance of common and specific patches. Utilizing these learned patches, we achieve superior performances on expression recognition compared to the state-of-the-arts. Index Terms—Cohn–Kanade, facial expression, GEMEP-FERA, group sparsity, MMI, multiscale facial patch selection