2D-human face recognition using SIFT and SURF descriptors of face's feature regions

Face recognition is the process of identifying people through facial images. It has become vital for security and surveillance applications and required everywhere including institutions, organizations, offices, and social places. There are a number of challenges faced in face recognition which includes face pose, age, gender, illumination, and other variable condition. Another challenge is that the database size for these applications is usually small. So, training and recognition become difficult. Face recognition methods can be divided into two major categories, appearance-based method and feature-based method. In this paper, the authors have presented the feature-based method for 2D face images. speeded up robust features (SURF) and scale-invariant feature transform (SIFT) are used for feature extraction. Five public datasets, namely Yale2B, Face 94, M2VTS, ORL, and FERET, are used for experimental work. Various combinations of SIFT and SURF features with two classification techniques, namely decision tree and random forest, have experimented in this work. A maximum recognition accuracy of 99.7% has been reported by the authors with a combination of SIFT (64-components) and SURF (32-components).

Automated summary

Face recognition is crucial for security and surveillance applications, but faces challenges such as face pose, age, gender, illumination, and small database sizes. The authors in this study used SURF and SIFT for feature extraction in 2D face images, achieving a maximum recognition accuracy of 99.7% with a combination of SIFT (64-components) and SURF (32-components).