Postprocessing for Skin Detection

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.

Multi-Sensor-Based Action Monitoring and Recognition via Hybrid Descriptors and Logistic Regression

“…Once human is successfully detected, the next stage is to identify the different joint locations for human body movement detection. We also applied a skin detection algorithm [39], along with boundary dilation and other morphological operations to extract human silhouettes. After silhouette monitoring and detection, the proposed system is able to extract the skeleton for further processing.…”