Abstract
We propose a reliable approach to detect skin regions that can be used in various human-related image processing applications. We use a color distance map, which itself is a grayscale image making the process simple, but still containing color information. Based on this map, we generate some skin as well as nonskin seed pixels, and then grow them to capture the appropriate regions. This approach outperforms the existing approaches in terms of segmenting solid and perfect skin regions. It does not generate much noisy segments. Moreover, it does not need any prior training session and can adapt to detect skin regions from images taken at different imaging conditions.
Highlights
Skin segmentation is a major component in humancomputer interaction- (HCI-) based applications such as gesture analysis, facial expression detection, face tracking, human motion tracking, and other human-related image processing applications in computer vision and multimedia such as filtering of web contents, retrieving in multimedia databases, video surveillance, videophone, and videoconferencing applications.The main target of skin detection is to detect skin pixels in images and thereby generate some skin regions
Combining Property 1 and Property 3 of distance map (DM), we find that if some skin regions exist in the image, a right half of Gaussian distribution is likely to exist in smaller gray levels in histogram of DM
To evaluate the strength of the proposed method and to compare with other well-established proposals, we have calculated three different criteria as presented in [29]: correct detection rate (CDR)—percentage of skin pixels correctly classified, false detection rate (FDR)—percentage of nonskin pixels incorrectly classified as skin pixels, and overall classification rate (CR)—percentage of pixels correctly classified
Summary
Skin segmentation is a major component in humancomputer interaction- (HCI-) based applications such as gesture analysis, facial expression detection, face tracking, human motion tracking, and other human-related image processing applications in computer vision and multimedia such as filtering of web contents, retrieving in multimedia databases, video surveillance, videophone, and videoconferencing applications. The key idea behind such approaches is that skin pixels’ coordinates will have similar values in appropriately chosen color space Such methods can be used right away without requiring any training phase. To make the explicit skin cluster classifiers flexible, a genetic algorithm-based technique is proposed in [9] It may not adapt with different images, since its thresholds are determined by a specific set of training pixels. The proposed method basically uses an explicit threshold-based skin cluster classifier and provides enhanced performance in varying imaging conditions. This improvement in performance is achieved by adaptively selecting the SSCbased on the test image, which avoids the use of fixed thresholds.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
