Facial expression recognition based on eigenspaces and principle component analysis Online publication date: Thu, 24-May-2018
by Ashim Saha; Sambhu Nath Pradhan
International Journal of Computational Vision and Robotics (IJCVR), Vol. 8, No. 2, 2018
Abstract: Facial expression detection or emotion recognition is one of the rising fields of research on intelligent systems. Emotion plays a significant role in non-verbal communication. An efficient face and facial feature detection algorithms plays as important role in indentifying of an emotion of a person at a particular moment. In this work, the authors implemented a system that recognises a person's facial expressions from the input images, using the algorithm of eigenspaces and principle component analysis (PCA). Eigenspaces are the face images which are projected onto a feature space that encodes the variation among known face images. PCA is used in this paper to make dimensional reduction of images in order to obtain a reduced representation of face images. The implementation is applied on three different facial expressions databases, extended Cohn-Kanade facial expression database, Japanese female facial expression database and self-made database in order to find out the effectiveness of the proposed method.
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