Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions
Skin detection is an important process in many applications like hand gesture recognition, face detection and ego-vision systems. This paper presents a new skin detection method based on a dynamic generation of the skin cluster range in the YCbCr color sp
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Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), 80131 Naples, Italy {nadia.brancati,giuseppe.depietro,maria.frucci, luigi.gallo}@cnr.it
Abstract. Skin detection is an important process in many applications like hand gesture recognition, face detection and ego-vision systems. This paper presents a new skin detection method based on a dynamic generation of the skin cluster range in the YCbCr color space, by taking into account the lighting conditions. The method is based on the identification of skin color clusters in the YCb and YCr subspaces. The experimental results, carried out on two publicly available databases, show that the proposed method is robust against illumination changes and achieves satisfactory results in terms of both qualitative and quantitative performance evaluation parameters. Keywords: Skin detection · Dynamic clustering · YCbCr colour space
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Introduction
Skin detection is an important issue in color image processing, which has been extensively studied over the years. It is a useful technique for the detection, segmentation and tracking of human skin in images or video streams. The interest in skin detection algorithms derives from their applicability to a wide range of applications such as gesture recognition, video surveillance, human computer interaction, ego-vision systems, human activity recognition [4, 6, 21], hand gestures detection and tracking [7, 8, 23, 31], nude images and video blocking [5, 26], feature extraction for content-based image retrieval [20], and age estima‐ tion [22]. Skin detection is a process that allows the extraction of candidate skin pixels in an image. In most cases, skin detection is performed by using pixel based techniques: a pixel is classified as a skin or non-skin pixel, independently from its neighbors, and only by using pixel color information. In addition, region based skin segmentation methods make use of extra information, for example spatial arrangement or texture information on the pixels detected in the skin detection process, to determine the boundaries of human skin regions [16, 17]. Therefore, a good pixel based method for skin detection can narrow the computational cost of the next process of segmentation, and, moreover, can improve the results of the segmentation. The main issue is to achieve a satisfactory skin detection under uncontrolled lighting conditions, since many applications, for example egovision systems, require the detection of skin human regions, both indoors and outdoors, with © Springer International Publishing AG 2017 V.V. Krasnoproshin and S.V. Ablameyko (Eds.): PRIP 2016, CCIS 673, pp. 27–35, 2017. DOI: 10.1007/978-3-319-54220-1_3
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high or low illumination conditions. Most approaches use specific color spaces to decorrelate chromatic components from luminance, since they are less sensitive to lighting conditions [9, 12]. However, some studies [10, 14] have shown that the luminance component plays an important role in skin detection and so it should not be
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