A Review of Discriminant Analysis Algorithms for Face Recognition Using Similarity Measures

Abstract

The Biometric, in a husky way, is using parts of your body as means of secured transaction in all means. Having stated this, there are numerous algorithms that have risen up in facilitating this job. Facial recognition is one such modality which has wide spread existence in the field of biometric. Face recognition involves solving wide variety of problems like pose, illumination, expression etc., Facial expression provides an important demeanour for studies major social and mental behaviour of humans. Facial recognition has recently become a challenging research area. Its applications include human emotion analysis and human computer interfaces. In this proposed methodology we propose a comparative experiment for facial expression recognition of human beings using different dimensionality reduction techniques and classifier methods. Fisher Linear Discriminant Analysis (FLDA) and Modular FLDA are used for feature extraction. Feature vector for the test image is compared with those of the train images. In this experiment we compared 17 distance measures and their modifications between feature vectors with respect to the recognition rates. The experimental results revealed that Modular FLDA produces the best recognition rate.