文章基本信息

标题：EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Face Recognition
其他标题：EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Face Recognition
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作者：Archana H. Sable ; Sanjay N. Talbar ; Haricharan Amarsing Dhirbasi 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2017
卷号：7
期号：4
页码：1923-1933
DOI：10.11591/ijece.v7i4.pp1923-1933
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Automatic recognition of people faces many challenging problems which has experienced much attention due to many applications in different fields during recent years. Face recognition is one of those challenging problem which does not have much technique to solve all situations like pose, expression, and illumination changes, and/or ageing. Facial expression due to plastic surgery is one of the additional challenges which arise recently. This paper presents a new technique for accurate face recognition after the plastic surgery. This technique uses Entropy based SIFT (EV-SIFT) features for the recognition purpose. 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 entropy is the higher order statistical feature. The corresponding EV-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 V- SIFT feature extracts the key points based on the volume of the structure. But the EV- SIFT method provides the contrast and volume information. This technique provides better performance when compare with PCA, normal SIFT and V-SIFT based feature extraction.
其他摘要：Automatic recognition of people faces many challenging problems which has experienced much attention due to many applications in different fields during recent years. Face recognition is one of those challenging problem which does not have much technique to solve all situations like pose, expression, and illumination changes, and/or ageing. Facial expression due to plastic surgery is one of the additional challenges which arise recently. This paper presents a new technique for accurate face recognition after the plastic surgery. This technique uses Entropy based SIFT (EV-SIFT) features for the recognition purpose. 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 entropy is the higher order statistical feature. The corresponding EV-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 V- SIFT feature extracts the key points based on the volume of the structure. But the EV- SIFT method provides the contrast and volume information. This technique provides better performance when compare with PCA, normal SIFT and V-SIFT based feature extraction.
关键词：Computer Science; image processing; pattern recognizing;EV-SIFT feature; face recognition; plastic surgery; SVM classification