Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Authentication based on bioinformatics
    (2004) Mohandas, M.K.; Shet, K.C.
    Authentication has assumed a lot of importance over the years due to hackers and unauthorised access. The Authentication based on bioinformatics will do away with all kinds of smart cards, identity cards or any other device being carried by the users. A lot of research is being done to improve the reliability of bioinformatics comparison with central database. This paper focuses on the research carried at NITK, Surathkal in this direction.
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    PCVOS: Principal component variances based off-line signature verification
    (Institute of Electrical and Electronics Engineers Inc., 2015) Arunalatha, J.S.; Prashanth, C.R.; Tejaswi, V.; Shaila, K.; Raja, K.B.; Anvekar, D.; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M.
    Offline signature verification system is widely used as a behavioral biometric for identifying a person. This behavioral biometric trait is a challenge in designing the system that has to counter intrapersonal and interpersonal variations. In this paper, we propose a novel technique PCVOS: Principal Component Variances based Off-line Signature Verification on two critical parameters viz., the Pixel Density (PD) and the Centre of Gravity (CoG) distance. It consists of two parallel processes, namely Signature training which involves extraction of features from the samples of database and Test signature analysis which performs extraction of features from the test samples. The trained values from the database are compared with the features of the test signature using Principal Component Analysis (PCA). The PCVOS algorithm shows a notable improvement over the algorithms in [21], [22] and [23]. © 2015 IEEE.
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    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.
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    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.