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Formulating face verification with semidefinite programming.
- Source :
-
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [IEEE Trans Image Process] 2007 Nov; Vol. 16 (11), pp. 2802-10. - Publication Year :
- 2007
-
Abstract
- This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters.
- Subjects :
- Humans
Information Storage and Retrieval methods
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Artificial Intelligence
Biometry methods
Face anatomy & histology
Image Enhancement methods
Image Interpretation, Computer-Assisted methods
Pattern Recognition, Automated methods
Subjects
Details
- Language :
- English
- ISSN :
- 1057-7149
- Volume :
- 16
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Publication Type :
- Academic Journal
- Accession number :
- 17990756
- Full Text :
- https://doi.org/10.1109/tip.2007.906271