Abstract
This paper presents a skin colour detection based on animproved dynamic threshold method to reduce false skin detection. Current fixedthreshold skin detection fails in certain situations such as misclassificationbetween non skin-like with similar skin-like colour. Any true skin may falsely bedetected as non-skin. Research work introduces high-level skin detection strategybased on online sampling where offline training is not required. This strategyshows a promising performance in term of classifying images under skin-like andethnicity image variations. However, some of the methods produced high falsepositives that reduced the accuracy of skin detection performance. Therefore,in this study, instead of single colour space and fixed threshold method, animproved skin detection based on multi-colour spaces is proposed. Furthermore,a dynamic threshold method also has been improved by introducing elasticelliptical mask model for online skin sampling. The experimental result showsan improvement in employing multi-colour rather than single colour space byreducing the false positive and increasing the precision rate.
Highlights
The human body is divided into many parts, and skin is one of it
The aim of this section is to evaluate the performance of the proposed skin colour detection method applied to different image conditions, skin tones and skin-like objects compared to the state-of-art works
Adopting dynamic skin colour detection using detected face as the skin sample is the easiest way to classify skin and non-skin under skin-like and ethnicity image variations. This is due to dynamic threshold values that are obtained individually from the detected face to be used in dynamic skin classification
Summary
The human body is divided into many parts, and skin is one of it. Skin is the largest organ in the human body. Skin colour detection has been studied extensively over the years and is frequently used in many applications such as in security, gaming and Human Computer Interaction (HCI). Applications such as face detection (Kovac et al, 2003), illicit content filtering (Fleck et al, 1996; Lee et al, 2006), facial recognition (Hsu et al, 2002), steganography (Cheddad et al, 2009) and Content-Based Image Retrieval (CBIR) (Mofaddel and Sadek, 2010; Wen et al, 2009) used skin detection as the primary step in their applications. The skin colour detection often affected by the image variation such as different illumination, skin-like objects, ethnicity, camera characteristics and complex background (Kakumanu et al, 2007)
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