Plastic Surgery Face Recognition: A comparative Study of Performance

The paper presents a comparative study of performance for face recognition algorithms in order to select the algorithms that have the highest performance and overcome the problems faced in recognition due to plastic surgery. A plastic surgery database that contains face images with different types of surgeries is used.. The work reports the performance evaluation of eleven photometric illumination techniques, five histogram normalization techniques and four feature extraction techniques. A minimum distance classifier has been adopted and four distance similarity measures were used. Face identification/verification techniques are considered in the present work. Experimental results were carried out and it can be concluded that for face identification, the best illumination technique is gradient-face (GRF) normalization technique, histruncate histogram normalization and the best feature extraction technique is gabor kernel fisher analysis GKFA. For face verification, the best illumination technique is gradient-face (GRF) normalization technique, and the best feature extraction technique is gabor principal component analysis (GPCA). For both face identification/verification the minimum distance classifier using Mahalanobis Cosine (MAHCOS) distance gives