Authors: P. Shanthi; S. Nickolas
Addresses: Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India
Abstract: Facial expression related to machine intelligence is a popular research area in emotion science, pain assessment, human behaviour analysis, virtual reality, etc. This paper aims at exploring a contour-based shape analysis from the viewpoint of geometric characteristics towards facial expression recognition. Since the facial landmark detection accuracy dramatically affects the final classification, a simple contour detection algorithm is used for identifying facial landmarks accurately. Spatial local and relative geometric features extracted with the neutral face as the reference are projected to the lower-dimensional space using stepwise linear discriminant analysis. The proposed system is tested and validated using backpropagation-based artificial neural network on JAFFE and MMI dataset with an average accuracy of 95.53% and 94.98%, respectively. The proposed scheme's recognition accuracy has been compared with the state-of-art methods, and the results show significant improvement in the proposed model over others using geometric features alone.
Keywords: emotion; facial expression; geometric features; stepwise linear discriminant analysis; SWLDA; backpropagation-based artificial neural network; BP-ANN.
International Journal of Biometrics, 2022 Vol.14 No.2, pp.138 - 154
Received: 17 Aug 2020
Accepted: 08 Nov 2020
Published online: 07 Apr 2022 *