Improving Face Detection Performance by Skin Detection Post-Processing

Face detection is already incorporated in many biometrics and surveillance applications. Therefore, the reduction of false detections is a priority in those systems. However, face detection is still challenging. Many factors, such as pose variation and complex backgrounds, contribute to false detections. Besides, the fidelity of a true detection, measured by precision rate, is a concern in content-based information retrieval. Following those issues, combinations of methods are developed focusing on balancing the trade-off between hit-rate and miss-rate. In this paper, we present an approach that improves face detection based on a post-processing of skin features. Our method enhanced the performance of weak detectors using a straightforward and low complex skin percentage threshold constraint. Furthermore, we also present a statistical analysis comparing our approach and two face detectors, under two different conditions for skin detection training, using a robust dataset for testing. The experimental results showed a significant drop in the number of false positives, reducing in 53%, while the precision rate was elevated in almost 5% when the Viola-Jones approach was used as face detector.