Postprocessing for Skin Detection

Journal of Imaging

Alessandra Lumini¹  Diego Baldissera²  Loris Nanni²  Sheryl Brahnam³ 

[1] Department of Computer Science and Engineering (DISI), University of Bologna, 47521 Cesena, Italy;Department of Information Engineering (DEI), University of Padova, 35131 Padova, Italy;Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO 65804, USA;

关键词: segmentation;    skin detector;    convolutional neural networks;    postprocessing;   

DOI  :  10.3390/jimaging7060095

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown