Surgically altered face detection using log-Gabor wavelet

Plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective and cosmetic surgeries modify the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. This paper employs an edge-based log-Gabor feature representation approach for the recognition of surgically altered faces. A log-Gabor based feature, termed as Edge-based Histogram of log-Gabor Magnitude Patterns (Edge-based HLGMP), is proposed and is very simple but effective. To ensure that the edge information richly captures significant features of the faces, a simple illumination normalization process is applied prior to edge information extraction. Experimental results on plastic surgery database shows that the proposed method performs well in comparison to existing plastic surgery face recognition methods reported in the literature.