Abstract
Facial expressions are used frequently in the social interaction and are considered important as they can reflect the inner emotional states of an individual. Automatic Facial Expression Recognition (FER) systems aim at classifying the facial images into various expressions. To do this task accurately, better feature descriptors are to be developed to effectively capture the facial information. The main contribution of this paper is our novel local texture based feature extraction techniques, inspired by Knight tour problem namely Knight Tour Patterns (kTP and KTP). kTP extracts two feature values in the 3 x 3 overlapping neighborhood, whereas, KTP extracts three feature values in the 5 x 5 overlapping neighborhood. To the proposed methods, apart from binary weights, different weights (fibonacci, prime, natural, squares and odd) have been applied to further reduce the feature vector length. The extensive experiments have been performed on JAFFE, MUG, TFEID, CK+ and KDEF datasets with respect to both six and seven expressions in person independent setup. The proposed methods are compared with the standard existing variants of binary patterns to demonstrate the efficiency of the proposed methods.
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Kartheek, M.N., Madhuri, R., Prasad, M.V.N.K., Bhukya, R. (2021). Knight Tour Patterns: Novel Handcrafted Feature Descriptors for Facial Expression Recognition. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_19
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