Facial expression recognition based on eigenspaces and principle component analysis. (2018)

• Record Type: Journal Article
• Authors: Saha, Ashim; Pradhan, Sambhu Nath
• Is Part Of: International journal of computational vision and robotics. Volume 8:Number 2(2018)
• Page Start: 190
• Page End: 200
• Publication Date: 2018
• Subjects: facial expression -- eigenspaces -- principle component analysis -- PCA -- emotion detection -- image processing; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

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.