Improved Face Recognition based on Hidden Markov Model
In this paper, a new face recognition technique based on Hidden Markov Model (HMM) Pre processing, and feature extraction (K means and the Sobel operator) is proposed. Two main contributions are presented; the first contribution in the pre processing were image's edges are normalized to enhance the HMM models to be non sensitive to different edges. The second contribution is a new technique to extract the image's features by splitting the image into non uniform height depending on the distribution of the foreground pixels. The foreground pixels are extracting by using the vertical sliding windows. The proposed technique is faster with a higher accuracy with respect to other techniques which are investigated for comparison. Moreover, it shows the capability of recognizing the normal face (center part) as well as face boundary