By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang
The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference lawsuits of the twelfth Asian convention on computing device imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions provided in those volumes used to be rigorously reviewed and chosen from 814 submissions. The papers are geared up in topical sections on reputation; 3D imaginative and prescient; low-level imaginative and prescient and lines; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster periods 1-3.
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Additional info for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part III
20 H. Li et al. – We outperform the state-of-the-art recognition accuracy over three challenging face recognition datasets. 2 Review of the PEP Model As we have mentioned, the proposed Eigen-PEP representation is built upon the PEP representation proposed by Li et al. [14,15]. The PEP representation can deal with a single face or a face set. The PEP representation itself has been shown to be robust to pose variations. When it is applied to video face recognition, a PEP model selects a set of image patches out of all video frames and concatenates the descriptors of the selected image patches into a single vector as the PEP representation.
Partial face recognition: alignment-free approach. T-PAMI 35, 1193–1205 (2013) 5. : Fast high dimensional vector multiplication based face recognition. In: ICCV (2013) 6. : Learning discriminant face descriptor. T-PAMI 36, 289–302 (2014) 7. : Similarity metric learning for face recognition. In: ICCV (2013) 8. : Visual classiﬁcation with multitask joint sparse representation. IEEE Trans. Image Process. 21, 4349–4360 (2012) 9. : Video-based face recognition via joint sparse representation. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) (2013) 10.
8, our method outperforms the state-of-theart algorithms on LFW. We also evaluate the performance of combining the joint Bayesian classiﬁer with the PEP representation. Since it is not practical to apply joint Bayesian classiﬁer over the high-dimensional PEP representation directly due to the large size of covariance matrices, we apply PCA to reduce the dimensionality of PEP representation to be 100 as well. To be fair, the PCA is trained separately over training PEP representations. 30 H. Li et al.