Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities
Submission deadline: 31. October 2017
Sponsored by IEEE
IEEE Access invites manuscript submissions in the area of ‘Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities’.
Visual surveillance is the latest paradigm of monitoring social security through machine intelligence. It includes the use of visual data captured by IR sensors, cameras placed in car, corridors, traffic signals etc. Visual surveillance facilitates the classification of human behavior, crowd activity, and gesture analysis to achieve application-specific objectives.
Biometrics is the science of uniquely identifying or verifying an individual among a set of people by exploring the user’s physiological or behavioral characteristics. Due to their ease of use in many application scenarios (including time attendance systems, border control, access control for high security, etc.), biometric systems are currently being introduced in many day-to-day activities.
Sometimes, algorithms developed for visual surveillance systems have been applied for biometric identification. Recently several research efforts have been devoted to merge these two technologies especially for adverse and covert scenarios. This special section will serve as a cross-platform to cover the recent advancements at the intersection of ‘visual surveillance’ and ‘biometrics’.
The topics of interest include (but they are not limited to):
• Real-time processing and recognition of humans from long surveillance videos
• Coping with complex illumination, large pose changes, occlusion and image blur in surveillance videos
• Emotion / Gesture / Activity Recognition and prediction
• Recognition at large distance in low resolution videos
• Summarization of surveillance videos and person re-identification
• Novel machine learning algorithms for biometrics under surveillance conditions
• Video frame quality evaluation approaches
• Image set modeling for video data analysis
• Extraction of soft-biometrics and attributes from surveillance videos
• Heterogeneous authentication / deauthentication using multi-modal surveillance data
• Efficient algorithms for massive video data analysis
• Deep learning and pattern recognition frameworks for biometric recognition
• Protocols and standards in surveillance biometrics
• Performance assessment and resource computation
• Adaptive biometric systems, biometric template update and security
• Biometrics for mobile platforms
• Biometric database management and indexing techniques
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Associate Editor (Contact for query):
Sambit Bakshi, National Institute of Technology Rourkela, India (firstname.lastname@example.org)
Guodong Guo, West Virginia University, USA
Hugo Proenca, University of Beira Interior, Portugal
Massimo Tistarelli, University of Sassari, Italy