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http://www.journals.elsevier.com ... oriented-biometrics
CFP - Video Surveillance-oriented Biometrics
Video surveillance-oriented biometrics is a very challenging task and has tremendous significance to the security of public places. With the growing threat of crime and terrorism to public security, it is becoming more and more critical to develop and deploy reliable biometric techniques for video surveillance applications. Traditionally, it has been regarded as a very difficult problem. The low-quality of video frames and the rich intra-personal appearance variations impose significant challenge to previous biometric techniques, making them impractical to real-world video surveillance applications. Fortunately, recent advances in computer vision and machine learning algorithms as well as imaging hardware provide new inspirations and possibilities. In particular, the development of deep learning and the availability of big data open up great potential. Therefore, it is the time that this problem be re-evaluated. This special issue will provide a platform for researchers to exchange their innovative ideas and attractive improvements on video surveillance-oriented biometrics. The following list suggests topics of interest (but not limited to): - Real-time processing and recognition of faces from long surveillance videos; - Solutions to complex illumination, large pose, occlusion and image blur in surveillance videos; - Solutions to large distance and low resolution face and gait recognition; - Summarization of surveillance videos; - Novel machine learning algorithms for biometrics under surveillance conditions; - Video frame quality evaluation approaches; - Image set modeling for video data analysis; - Face detection, tracking, and alignment in surveillance video frames; - Computation of soft-biometrics and attributes from surveillance videos; - Announcement of new video surveillance databases for biometrics; - Heterogeneous face recognition using multi-modal surveillance data; - Efficient algorithm for massive video data analysis; - Survey papers regarding the status and trends for surveillance video-based biometrics.
TENTATIVE SCHEDULE Submission period: 1st March 2017 to 15th April 2017; First review notification: 1st June 2017; Revision submission: 16th July 2017; Second review notification: 1st September 2017; Acceptance notification: 16th October 2017.
SUBMISSION INSTRUCTIONS Authors are invited to register at http://ees.elsevier.com/prletters/ and submit their papers electronically during the submission period. When uploading their papers through the online system, authors should select the acronym SI:VSB as the article type. Papers should be prepared in a format consistent with the PRLetters submission guidelines. The maximal length of a paper is 10 pages in the PRLetters layout and may become 11 in the revised version if referees explicitly request additions. The submitted papers should not have been published previously, nor being under consideration for publication elsewhere. If one submission is the extended work of one conference paper, the original work should be included and a description of the changes should be provided. The PRLetters submission should include at least 30% new contribution (more experiments, proofs of theorems not included in the conference paper, more comparisons with other methods in the literature and so on); and the title of the PRLetters paper should be different, the same figures cannot be used and the common part of the conference paper and of the extended version cannot be verbatim the same. The review process will follow the standard PRLetters scheme. Each paper will be reviewed by at least two referees and two reviewing rounds will be possible in general, out of which major revision is possible only for the first round. Submissions will probably being rejected if major revision is still required after the second round of review.
GUEST EDITORS Dr. Changxing Ding School of Electronics & Information Engineering, South China University of Technology, Prof. Kaiqi Huang National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Prof. Vishal M. Patel Department of Electrical and Computer Engineering, Rutgers University, US, Prof. Brian C. Lovell School of Information Technology and Electrical Engineering, the University of Queensland, Australia,
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