Enhanced human skin colour retrieval system using RGB ratio model / Ghazali Osman, Mohammad Suzuri Hitam, Mohd Pouzi Hamzah and Noor Maizura Mohd Noor

• Main Authors: Osman, Ghazali, Hitam, Mohammad Suzuri, Hamzah, Mohd Pouzi, Mohd Noor, Noor Maizura
• Format: Article
• Language: English
• Published: Faculty of Information Management, Universiti Teknologi MARA 2012
• Subjects: Optical data processing; Research. Information retrieval. Information behavior. Information literacy; Electronic information resource searching
• Online Access: https://ir.uitm.edu.my/id/eprint/50704/1/50704.PDF; https://ir.uitm.edu.my/id/eprint/50704/; https://ijikm.uitm.edu.my/
• Institution: Universiti Teknologi Mara

Skin colour detection is frequently used for searching people, face detection, pornographic filtering and hand tracking. The presence of skin or non-skin in digital image can be determined by manipulating pixels' colour and/or pixels' texture. The main challenge of skin colour detection is to develop a classifier that is robust to the large variations in skin colour appearance. This process is difficult because the appearance of a skin colour in an image depends on the illumination conditions where the image was captured. Therefore, the main problem in skin colour detection is to represent the skin colour distribution model that is invariant or least sensitive to changes in illumination condition. Another problem comes from the fact that many objects in the real world may possess almost similar skin­ tone colour such as wood, leather, skin-coloured clothing, hair and sand. Moreover, skin colour is different between races and can be different from a person to another, even with people of the same ethnicity. Finally, skin colour will appear a little different when different types of camera are used to capture the object or scene. The objective of this study is to develop a skin colour classifier based on pixel-based using RGB ratio method. This skin classifier was tested with Sldb dataset and two benchmark datasets; UChile and TOSO datasets to measure classifier performance. The performance of skin classifier was measured based on true positive (TF) and false positive (FP) indicator. The RGB ratio model is a newly proposed method that belongs under the category of an explicitly defined skin region model. This newly proposed model was compared with Kovac, Saleh and Swift models. The experimental results showed that the RGB ratio model outperformed all the other models in term of detection rate. The RGB ratio model is able to reduce FP detection that caused by reddish objects colour as well as be able to detect darkened skin and skin covered by shadow.