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Selection of Distinctive SIFT Feature Based on its Distribution on Feature Space and Local Classifier for Face Recognition

2010
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Tong Liu, Sung-Hoon Kim, Sung-Kil Lim, Hyon-Soo Lee, IAJIT
Dibuat : 2012-06-23, dengan 1 file

Keyword : Face recognition, SIFT, distinctive features.
Subjek : Selection of Distinctive SIFT Feature Based on its Distribution on Feature Space and Local Classifier for Face Recognition
Url : http://www.ccis2k.org/iajit/PDF/vol.10,no.1/4161-2.pdf
Sumber pengambilan dokumen : Internet

This paper investigates a face recognition system based on SIFT (Scale Invariant Feature Transform) feature and


its distribution on feature space. The system takes advantage of SIFT which possess strong robustness to expression, accessory


pose and illumination variations. Since we use each of SIFT keypoint as the feature of face and SIFT keypoints are very


complicated in feature space, we apply the feature partition on SOM (Self Organizing Map) and adopt local MLP (Multilayer


Perceptron) for each node on map to improve the classification performance. Moreover the distinctive features from all SIFT


keypoints in each face class are defined and extracted based on feature distribution on SOM. Finally the face can be


recognized through the proposed scoring method depending on the classification result of these distinctive features. In the


experiments, the proposed method gave a higher face recognition rate than other methods including matching and holistic


feature based methods in three famous databases

Deskripsi Alternatif :

This paper investigates a face recognition system based on SIFT (Scale Invariant Feature Transform) feature and


its distribution on feature space. The system takes advantage of SIFT which possess strong robustness to expression, accessory


pose and illumination variations. Since we use each of SIFT keypoint as the feature of face and SIFT keypoints are very


complicated in feature space, we apply the feature partition on SOM (Self Organizing Map) and adopt local MLP (Multilayer


Perceptron) for each node on map to improve the classification performance. Moreover the distinctive features from all SIFT


keypoints in each face class are defined and extracted based on feature distribution on SOM. Finally the face can be


recognized through the proposed scoring method depending on the classification result of these distinctive features. In the


experiments, the proposed method gave a higher face recognition rate than other methods including matching and holistic


feature based methods in three famous databases

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