Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Speed-Invariant Gait Recognition Using Correlation Factor Lists for Classroom Attendance Systems(Springer Science and Business Media Deutschland GmbH, 2024) Anusha, R.; Jaidhar, C.D.The way a person walks is an important biometric used in many human detection applications, including classroom attendance systems. In such applications, speed is one of the key factors that can affect the performance of a gait detection system, as the student will enter the classroom at different speeds, depending on various factors. This study proposes an effective approach to reduce the impact of speed variations in a gait detection system. Initially, the proposed approach identifies similar regions between training and test samples. Later, the correlation factor lists are calculated using three proposed features: intensity measure, contour measure, and spatial measure. By capturing minute variations in static data, this method efficiently enhances the performance of a gait detection system. The evaluation of this approach uses CASIA C and OU-ISIR A datasets of gait. The experimental results suggest that this approach shows potential in comparison to other gait recognition methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item FASE Module Enabled Recognition of Individuals Using Distinct Gait Patterns(Institute of Electrical and Electronics Engineers Inc., 2024) Anusha, R.; Jaidhar, C.D.Extensive research has been conducted on gait, the walking pattern, and multiple methods have been created to utilize it as a biometric for identifying individuals. Nevertheless, there has been limited exploration of identifying individuals in running videos. A novel method is introduced in the paper that extends the feature-based approach to recognize individuals by their running patterns. The gait recognition performance is boosted in this work through the introduction of the Feature Analysis and Sample Elimination (FASE) module, which selects significant data samples using cluster formation, analysis, and elimination. Later on, the assignment of a testing sample to the training sample is achieved through the use of the proposed classification method. The experiments utilize the KTH, OU-ISIR A, and Weizmann databases. The obtained experimental results showcase the effectiveness of the proposed method. © 2024 IEEE.