Abstract
Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences.
Highlights
Making fair comparisons between the skin detection algorithms and datasets is problematic, because many datasets are created by different research groups, have unreliable ground truths, and are intended for many separate applications
The performance is the average result considering all the frames for a given video; in other words, each video is viewed as one image when it comes to reporting the performance; (6) MCG [39]: a dataset containing 1000 images; (7) VMD [41]: a dataset that includes 285 images collected from a number of different datasets that are publicly available for human action recognition; (8) SFA [42]: a dataset that contains 3354 skin samples and 5590 nonskin samples extracted from two popular face recognition datasets: the FERET database and the AR
A new post-processing method for enhancing the classification of skin detectors was presented in this work that assigns a class to an image and applies either a series of morphological operators or a homogeneity function to generate the final mask
Summary
J. A highly relevant segmentation problem is skin detection, a problem that discriminates regions in images and videos into the two classes skin and nonskin. The applications of skin detection are many, as are the challenges. Skin detection is a valuable component in locating faces, tracking individuals, human–computer interactions, biometric authentication, medical imaging, and objectionable content screening [1]. Challenges are concerned with building powerful classifiers and with developing all the additional methods required to accomplish the task, including data preprocessing and postprocessing
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.
