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Face recognition is one of the challenging problems which suffer from practical issues like pose, expression, and illumination changes, and/or aging. Plastic surgery is one among the issues that poses great difficulty in recognizing the faces. The literature has been reported with traditional features and classifiers for recognizing the faces after plastic surgery. In order to reduce the computational complexity of high-dimensional feature descriptor and improve the accuracy of recognition algorithm, the paper proposes Volume based SIFT (V-SIFT) for accurate face recognition after the plastic surgery. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the volume is the higher order statistical feature. The corresponding V-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the VSIFT feature extracts the key points based on the volume of the structure. Thus V-SIFT provide better performance when compared with PCA and normal based feature extraction. The effectiveness of the algorithm is verified by experiments on the ORL face image database, which demonstrates good stability and robustness especially under the conditions of some confounding factors such as different facial expressions, postures and so on.

international journal for research in applied science and engineering technology

Application of Integral Transform to Recognition of Plastic Surgery Faces and the Surgery Types: an Approach with Volume based Scale Invariant Features and SVM