2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Scale Invariant Feature Transform (SIFT) proposed by Lowe has been widely and successfully applie... more Scale Invariant Feature Transform (SIFT) proposed by Lowe has been widely and successfully applied to object detection and recognition. However, the representation ability of SIFT features in face recognition has rarely been investigated systematically. In this paper, we proposed to use the person-specific SIFT features and a simple nonstatistical matching strategy combined with local and global similarity on key-points clusters to solve face recognition problems. Large scale experiments on FERET and CAS-PEAL face databases using only one training sample per person have been carried out to compare it with other non person-specific features such as Gabor wavelet feature and Local Binary Pattern feature. The experimental results demonstrate the robustness of SIFT features to expression, accessory and pose variations.
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Scale Invariant Feature Transform (SIFT) proposed by Lowe has been widely and successfully applie... more Scale Invariant Feature Transform (SIFT) proposed by Lowe has been widely and successfully applied to object detection and recognition. However, the representation ability of SIFT features in face recognition has rarely been investigated systematically. In this paper, we proposed to use the person-specific SIFT features and a simple nonstatistical matching strategy combined with local and global similarity on key-points clusters to solve face recognition problems. Large scale experiments on FERET and CAS-PEAL face databases using only one training sample per person have been carried out to compare it with other non person-specific features such as Gabor wavelet feature and Local Binary Pattern feature. The experimental results demonstrate the robustness of SIFT features to expression, accessory and pose variations.
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