Pattern Recognition to Enhance Video Based Human Identification for Advanced Security


  • A. Saravanan Department of EEE, SMK Fomra Institute of Technology, Fomra Nagar, OMR IT Highway, Kelambakkam, Chennai 60103.
  • R. Benschwartz Associate Professor, Department of Electronics and Communication Engineering, Mar Ephraem College of Engineering and Technology, Kanyakumari.
  • R. Roopa Assistant Professor, Department of CSE (Data Science), Madanapalle Institute of Technology & Science, Kadiri Road Angallu, Madanapalle, Andhra Pradesh 517325.
  • M. Maheswari Associate Professor, Department of Computer Science and Engineering, Panimalar Engineering College, Chennai.
  • K. B. Kishore Mohan Associate Professor, Department of Biomedical Engineering, Saveetha Engineering College, Chennai.


Video surveillance, pattern recognition, gait recognition, Honda/UCSD database, feature extraction


Scholars investigating computer vision are getting more engaged in human acceptance at a distance. In simple terms, gait identification aims to deal with this problem by determining persons solely depending on their gait patterns. This work introduces a spatial-temporal silhouette analysis according to a gait identification system that is both simple and efficient. For any series of images, a background subtraction Initial, the fluctuating silhouettes of a pedestrian individual are differentiated and recorded employing an algorithm and an ordinary correspondence approach. We proposed a pattern recognition methodology that can avoid fraud and precisely identify person silhouettes in videos, even at a distance. CCTV cameras often offer low-quality video, which can make gathering forensic evidence difficult. Online assessments featuring live video have been carried out on a database consisting of 22 unseen pretenders and 50 enrolled human beings. Using an erroneous accept rate of 0.0014, the recommended strategy obtained a 100% verification rate and a 97.8% recognition rate. On the contrary, studies with the Honda/UCSD database were carried out as well and an approximate 99 % identification rate was reached.


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How to Cite

Saravanan, A. ., Benschwartz, R. ., Roopa, R. ., Maheswari, M. ., & Mohan, K. B. K. . (2024). Pattern Recognition to Enhance Video Based Human Identification for Advanced Security. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 911–920. Retrieved from



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