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The Computer Vision and Pattern Recognition Group conducts research and invents technologies that result in commercial products that enhance the security, health and quality of life of individuals the world over.

We leverage USF's strengths in Video and Image Analysis Technology, Biometric Technology, Classification and Knowledge Discovery, Affective Computing , VR/AR, HCI, and Medical Data Analysis Technology to impact:

  • Domestic Security: Early detection and identification of suspicious activities, authentication of persons prior to permitting access to secure facilities, automated analysis of surveillance video for abnormal patterns, automated monitoring of coastal waters.

  • Quality of Life:¬†Automated interpretation of sign language to improve communication with the handicapped, understanding the formation of red tide leading to methods of prevention or amelioration, automated system to detect and prevent collision of vehicles, algorithms for automated video analysis to enable content indexing and search, prediction of autism in children, analysis of pain and stress in soldiers.

  • Healthcare: Machine learning methods to spot disease outbreaks, understanding of gene expressions leading to development of early detection and treatment of diseases, analysis of medical images, tissue classification from Magnetic Resonance images for improved diagnostics, interpretation of brain waves to interact with computers and prosthetics, assessment of pain in neonates.

Please feel free to explore our site and learn more about current and past projects, participating researchers, publications and presentations.

Recent Publications

R. Subramanian and S. Sarkar, "Evaluation of Algorithms for Orientation Invariant Inertial Gait Matching", IEEE Transactions on Information Forensics and Security, 14(2): 304-318, 2019

G. Zamzmi, D. Goldgof, R. Kasturi, and Y. Sun, "Toward Ubiquitous Assessment of Neonates' Health Condition", UbiComp Workshops, 2018

D. Fabiano and S. Canavan, "Spontaneous and Non-spontaneous 3D Facial Expression Recognition using a Statistical Model with Global and Local Constraints", ICIP, 2018

G. Zamzmi, R. Kasturi, D. Goldgof, R. Zhi, T. Ashmeade, and Y. Sun, "A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases", IEEE Reviews in Biomedical Engineering, 11, pp. 77-96, 2018