Automatic Fiducial Points Detection for Multi-facial Expressions via Invariant Features and Multi-layer Kernel Sliding Perceptron

In recent years, automatic facial expression recognition (FER) is a primary processing method of non-verbal communication and conveys their intention states among human–machine interaction. In this paper, we have proposed a novel FER system that wisely detects automatic fiducial points, generates robust multi-perspective views facial masks and recognizes expressions via kernel sliding perceptron. Initially, we detect multiple faces in a scene via saliency factor and detect 38 fiducial points by connecting maximum interest points in each face. These points are used for generating a face mask by measuring triangles formation and B-spline curve fitting. Then, we extract invariant features, such as fused HOG–LBP, advance 0°–180° intensity and fast marching features, and seek the best points’ junction optimizer with an artificial bee colony algorithm. Finally, we propose a novel multi-layer kernel sliding perceptron method to classify six basic facial expressions. The proposed system outperforms the existing well-known statistical state-of-the-art FER methods in terms of recognition accuracy of 91.05% over Chicago Faces and 88.50% over Fam2a datasets, respectively. The proposed system has a possible broader impact and potential applications of FER for multimodal intelligent systems.