Abstract Automated human emotion detection is a topic of significant interest in the field of computer vision. Over the past decade, much emphasis has been on using facial expression recognition (FER) to extract emotion from facial expressions. Many popular appearance-based methods such as local binary pattern (LBP), local directional pattern (LDP) and local ternary pattern (LTP) have been proposed for this task and have been proven both accurate and efficient. In recent years, much work has been undertaken into improving these methods. The gradient local ternary pattern (GLTP) is one such method aimed at increasing robustness to varying illumination and random noise in the environment. In this paper, GLTP is investigated in more detail and further improvements such as the use of enhanced pre-processing, a more accurate Scharr gradient operator, dimensionality reduction via principal component analysis (PCA) and facial component extraction are proposed. The proposed method was extensively tested on the CK+ and JAFFE datasets using a support vector machine (SVM) and shown to further improve the accuracy and efficiency of GLTP compared to other common and state-of-the-art methods in literature.
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