Soft Biometrics Authentication: A Cluster-Based Skin Color Classification System

Abdou-Aziz Sobabe, Tahirou Djara, Blaise Blochaou, Antoine Vianou

Source Title: Journal of Information Technology Research (JITR)15(1)

ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.298620

MLA

Sobabe, Abdou-Aziz, et al. "Soft Biometrics Authentication: A Cluster-Based Skin Color Classification System." JITR vol.15, no.1 2022: pp.1-17. http://doi.org/10.4018/JITR.298620

APA

Sobabe, A., Djara, T., Blochaou, B., & Vianou, A. (2022). Soft Biometrics Authentication: A Cluster-Based Skin Color Classification System. Journal of Information Technology Research (JITR), 15(1), 1-17. http://doi.org/10.4018/JITR.298620

Chicago

Sobabe, Abdou-Aziz, et al. "Soft Biometrics Authentication: A Cluster-Based Skin Color Classification System," Journal of Information Technology Research (JITR) 15, no.1: 1-17. http://doi.org/10.4018/JITR.298620

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

This manuscript presents the design of a new approach of human skin color authentication. Skin color is one of the most popular soft biometric modalities. Since a soft biometric modality alone cannot reliably authenticate an individual, this new system is designed to combine skin color results with other pure biometric modalities to increase recognition performance. In the classification process, we first perform facial skin detection by segmentation using the thresholding method in the HSV color space. Then, the K-means algorithm of the clustering method is used to determine the dominant colors on the skin pixels in the RGB model. Variations according to the R, G and B components are recorded in a reference model to enable an individual’s identity to be predicted on the basis of 30 clusters. Experimental results are promising and give a false acceptance rate (FAR) of 29.47% and a false rejection rate (FRR) of 70.53%.