This chapter presents a robust face recognition technique which is based on the extraction of Scale Invariant Feature Transform (SIFT) features from the face areas. It uses both a global and local matching strategy. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. The Dempster–Shafer decision theory is applied to fuse the two matching strategies. The proposed technique has been evaluated with the Indian Institute of Technology Kanpur (IITK), Olivetti Research Laboratory (ORL) (formerly known as AT&T face database), and the Yale face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in cases of partially occluded faces or with missing information. Besides this, some state-of-the-art face recognition techniques have been presented and the current face-matching technique is compared with those techniques while all the matching techniques use SIFT descriptors as local features.

This chapter presents a robust face recognition technique which is based on the extraction of Scale Invariant Feature Transform (SIFT) features from the face areas. It uses both a global and local matching strategy. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. The Dempster–Shafer decision theory is applied to fuse the two matching strategies. The proposed technique has been evaluated with the Indian Institute of Technology Kanpur (IITK), Olivetti Research Laboratory (ORL) (formerly known as AT&T face database), and the Yale face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in cases of partially occluded faces or with missing information. Besides this, some state-of-the-art face recognition techniques have been presented and the current face-matching technique is compared with those techniques while all the matching techniques use SIFT descriptors as local features.

Face Recognition Using Global and Local Salient Features / Kisku DR; Gupta P; Sing JK; Tistarelli M. - (2012), pp. 453-477. [10.1007/978- 0-85729-439-5_16]

Face Recognition Using Global and Local Salient Features

TISTARELLI, Massimo
2012

Abstract

This chapter presents a robust face recognition technique which is based on the extraction of Scale Invariant Feature Transform (SIFT) features from the face areas. It uses both a global and local matching strategy. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. The Dempster–Shafer decision theory is applied to fuse the two matching strategies. The proposed technique has been evaluated with the Indian Institute of Technology Kanpur (IITK), Olivetti Research Laboratory (ORL) (formerly known as AT&T face database), and the Yale face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in cases of partially occluded faces or with missing information. Besides this, some state-of-the-art face recognition techniques have been presented and the current face-matching technique is compared with those techniques while all the matching techniques use SIFT descriptors as local features.
978-0-85729-438-8
This chapter presents a robust face recognition technique which is based on the extraction of Scale Invariant Feature Transform (SIFT) features from the face areas. It uses both a global and local matching strategy. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. The Dempster–Shafer decision theory is applied to fuse the two matching strategies. The proposed technique has been evaluated with the Indian Institute of Technology Kanpur (IITK), Olivetti Research Laboratory (ORL) (formerly known as AT&T face database), and the Yale face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in cases of partially occluded faces or with missing information. Besides this, some state-of-the-art face recognition techniques have been presented and the current face-matching technique is compared with those techniques while all the matching techniques use SIFT descriptors as local features.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11388/71485
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