Abstract—Similarity feature-based selection and classification (SFSC) algorithms, introduced by Tran et al. in 2013, have been used as a tool to reduce storage cost and increase performance of face recognition systems. However, these still exist a problem when automatically selecting a suitable threshold. This paper introduces a new approach, which combines SFSC algorithms, and a wrapper model, to automatically select a suitable threshold and improve face recognition accuracy. The training face image set (which is split into two separated subsets including a training subset and a wrapper subset) is utilized as data input for the similarity feature-based selection algorithm in combination with the wrapper model to identify a best feature set. The obtained feature set will be used for classification. The experiments were conducted on the histogram-based feature and two databases, ORL database of faces and Georgia Tech face database. The results demonstrated that the proposed algorithm not only allowed for automatic detection of the suitable feature set, but also achieved a better recognition accuracy compared to conventional algorithms.
Index Terms—Face recognition, similarity feature, feature selection, filter model, wrapper model.
Chi-Kien Tran is with the Faculty of Information Technology, Hanoi University of Industry, No. 298, Cau Dien Street, Bac Tu Liem district, Hanoi, Vietnam (e-mail: email@example.com).
Cite: Chi-Kien Tran, "Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 357-362, 2019.