Face Detection and Count Estimates in Surveillance Color Images

Abstract

Face detection has various applications in many areas, an important example of which is security- related surveillance in confined areas. Once an image is analyzed for faces detection, the faces in the image are tallied. In this paper, color segmentation is used as a first step in the face detection process followed by grouping likely face regions into clusters of connected pixels. Median filtering is then performed to eliminate the small clusters and the resulting blobs are matched against a face pattern (ellipse) subjected to constraints for rejecting non-face blobs. The system was implemented and validated via numerical test cases for images with different formats, sizes, number of people, and complexity of the image background. Numerical results suggest that the proposed approach correctly detects and counts non-dark faces with reasonable background complexity.