Abstract
Plastic surgeries alter the original facial features thereby posing a great challenge for face recognition algorithms. To address this problem, a geometrical face recognition after plastic surgery GFRPS system is proposed in this paper. The recognition process is performed in three steps; localising the regions of interest ROIs of the 'After' image, measuring the geometrical distances between the ROIs centres to determine the post-geometrical features vector, and using a minimum distance classifier to compare the post-features vector with the pre-features vectors database to find the perfect matching. The main advantage of the proposed system is its simplicity besides its high performance. The experimental results reveal that the proposed technique achieves much higher face identification rate than the best known results in the literature beside its high robustness under different types of plastic surgery procedures. The proposed technique provides average identification rate of 78.5% for local plastic surgery and 76.1% for global surgery.
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