By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference complaints 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 offered in those volumes used to be rigorously reviewed and chosen from 814 submissions. The papers are equipped in topical sections on acceptance; 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 resources for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part III

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Springer, Heidelberg (2008) 25. : A Computational Approach To Edge Detection. IEEE Trans. Pattern Anal. Mach. Intell. , San Jose, USA Abstract. To effectively solve the problem of large scale video face recognition, we argue for a comprehensive, compact, and yet flexible representation of a face subject. It shall comprehensively integrate the visual information from all relevant video frames of the subject in a compact form. It shall also be flexible to be incrementally updated, incorporating new or retiring obsolete observations.

After that, for each gallery subject, we build one Eigen-PEP from all his/her videos as the representation of the subject. Since the identity information is available, instead of following Eq. 8, we follow Chen et al. [16] to train the joint Bayesian classifier. 80 75 70 65 60 55 50 45 Cumulative Rate (%) H. Li et al. Cumulative Rate (%) 26 Eigen-PEP MTJSR 1 2 3 4 5 6 Rank 7 8 80 75 70 65 60 55 50 45 40 35 9 10 1 Eigen-PEP MTJSR 1 2 3 4 5 6 7 8 Rank (c) close-set 500 subjects 2 3 4 5 6 Rank 7 8 9 10 (b) close-set 200 subjects Cumulative Rate (%) Cumulative Rate (%) (a) close-set 100 subjects 70 65 60 55 50 45 40 35 30 25 Eigen-PEP MTJSR 9 10 65 60 55 50 45 40 35 30 25 20 Eigen-PEP MTJSR 1 2 3 4 5 6 7 8 9 10 Rank (d) close-set 1000 subjects Fig.

We then adopt the joint Bayesian classifier [16] to implement face recognition based on the Eigen-PEP representation. The high-level work-flow of the video face recognition system based on Eigen-PEP is summarized in Fig. 2. We utilize the PEP model [14,15] and PCA to construct the Eigen-PEPs for videos (Sect. 2). In the training stage, the joint Bayesian classifier [16] is trained from a set of matched and mismatched video pairs (Sect. 3) represented in the Eigen-PEPs. The classifier is then applied to compare two face videos in the testing stage for either video face verification or identification.

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