Detection of side-view faces in color images

Abstract

A coarse-to-fine scheme for the detection of sideview faces in color images is proposed in this paper, which extends the current state of the art of face detection research. The input image can be of complex scene, containing cluttered background and confusing objects. The system consists of four stages, each of which is a refinement of the previous one, namely: (1) skin-tone detection by color, (2) region and edge preprocessing with morphological operations and length filtering, (3) face candidate region selection based on normalized similarity value and (4) final verification using hidden Markov models. Encouraging experimental results have been obtained, due to the utilization of multiple features of the input image and the conjunction of employment of various image processing and pattern recognition techniques. Besides providing the ability to detect faces other than frontal-view, our work has 3 original contributions, including the Normalized Similarity Value (NSV) to detect the presence of a given curve pattern, the iterative partition process to segment the object from confusing extraneous regions for higher detection accuracy and the exploration of the use of HMM to recognize objects in images.