Skin Detection for Reducing False Positive in Face Detection

Skin detection is widely used in several applications ranging from tracking body parts to hand gesture analysis and face detection. In this chapter, we investigate and evaluate the usefulness of a skin detector to reduce the number of false positives found by an ensemble of face detectors. The fusion of different face detectors permits on one hand to maximize the number of true positives found by the system, on the other (unfortunately) to increase also the number of false positives. To overcome this shortcoming difficulty, in this work we propose and test several filtering steps based firstly on skin detection, secondly on eye detection and when available on the depth map. In this chapter, we investigate and evaluate an ensemble of approaches for skin detection based on different classifiers, color space and color constancy pre-processing. The proposed skin detection filtering step is first validated and compared with other state-of-the-art approaches on different skin datasets: a benchmark based on 25 videos of 8991 images with manually annotated pixel-level ground truth, the MCG-Skin benchmark dataset, the Pratheepan’s dataset and the UChile dbskin2 dataset. Then skin detection filtering step is evaluated for face detection purposes on two face datasets (one including both 2D and depth data). Experimental results confirm that our proposed approach obtains a very good performance. A MATLAB version of the filtering steps and the dataset used in this paper will be freely available from (https://www.dei.unipd.it/node/2357 + Pattern Recognition and Ensemble Classifiers)