Deep Forest Approach for Facial Expression Recognition

Abstract

Facial Expression Recognition is a prospective area in Computer Vision (CV) and Human-Computer Interaction (HCI), with vast areas of application. The major concept in facial expression recognition is the categorization of facial expression images into six basic emotion states, and this is accompanied with many challenges. Several methods have been explored in search of an optimal solution, in the development of a facial expression recognition system. Presently, Deep Neural Network is the state-of-the-art method in the field with promising results, but it is incapacitated with the volume of data available for Facial Expression Recognition task. Therefore, there is a need for a method with Deep Learning feature and the dynamic ability for both large and small volume of data available in the field. This work is proposing a Deep Forest tree method that implements layer by layer feature of Deep Learning and minimizes overfitting regardless of data size. The experiments conducted on both Cohn Kanade (CK+) and Binghamton University 3D Facial Expression (BU-3DFE) datasets, prove that Deep Forest provides promising results with an impressive reduction in computational time.