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

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AuteurDJARA, TAHIROU
AuteurSOBABE, ABDOU AZIZ
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2022
ResumeThis 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, the authors 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%.
DOI10.4018/JITR.298620
Autre identifiantBECDB-16852
URIhttps://dspace.uac.bj/handle/123456789/14084
Languefr
Fait partie deJournal of Information Technology Research
SujetClustering Method
SujetHSV Color Space
SujetImage Classification
SujetK-Means Algorithm
SujetRGB Image
SujetSkin Detection
SujetSkin Dominant Colors
SujetSkin Segmentation
SujetUser Authentication
TitreSoft Biometrics Authentication: A Cluster-Based Skin Color Classification System
TypeArticle

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