Automatic facial feature detection for model-based coding

Abstract

This paper presents an automatic facial feature detection system for 3D model based coding applications. This proposed system is based on simple image processing techniques, which can be easily implemented using parallel algorithms is parallel processing hardware. Model Based Face Coding can be used in remote teaching to enhance the quality of remote teaching, where by reducing the barrier between teacher and student. In this case, only a selected set of control points of the face is transmitted to the remote terminal instead of sending video signal. In order to extract this set of control points a predefined 3D generic wire frame model is used. In this paper, automatic extraction process of the feature points of facial images needed for 3D model fitting is discussed. The proposed detection methods for all the facial features utilize filtering, thresholding, edge detection and edge counting without any manual adjustments or initialization. Head top, chin points, eye center, mouth center and nose center were detected using vertical integral projection method. The centroid method was used successfully for eyebrow center detection. Four points of the mouth features were detected with both Canny edge detection method and amplitude projection method. The first one had limited success and second gave very satisfactory results. On the whole, the results obtained are encouraging and could be used in automatic registration of 2D facial images into 3D face models. Subsequent tracking of some of these feature points lead us to automatic facial expression recognition using optical flow.