Faculty Publications

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Publications by NITK Faculty

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    Identifying Humans Through Gait Features
    (Springer Science and Business Media Deutschland GmbH, 2024) Anusha, R.; Jaidhar, C.D.
    Achieving robust human identification in visual surveillance is an ongoing and open research challenge in biometrics. In recent years, gait has added attention for its unique benefits when matched to other biometrics. Different gait-challenging conditions hinder the performance of gait recognition systems in real-world scenarios. The only solution to solve these challenges is to develop suitable features using available information sources. Enhancing the gait recognition system’s performance is the goal of this research, with a focus on frontal, speed-invariant, and clothing-invariant recognition. The proposed approaches demonstrate their capabilities through experimental results, outperforming existing methods of gait recognition. The solutions proposed in this paper increase gait recognition performance, making it applicable in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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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.
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    Human gait recognition based on histogram of oriented gradients and Haralick texture descriptor
    (Springer, 2020) Anusha, R.; Jaidhar, C.D.
    Gait recognition is an evolving technology in the biometric domain; it aims to recognize people through an analysis of their walking pattern. One of the significant challenges of the appearance-based gait recognition system is to augment its performance by using a distinctive low-dimensional feature vector. Therefore, this study proposes the low-dimensional features that are capable of effectively capturing the spatial, gradient, and texture information in this context. These features are obtained by the computation of histogram of oriented gradients, followed by sum variance Haralick texture descriptor from nine cells of gait gradient magnitude image. Further, the performance of the proposed method is validated on five widely used gait databases. They include CASIA A gait database, CASIA B gait database, OU-ISIR D gait database, CMU MoBo database, and KTH video database. The experimental results demonstrated that the proposed approach could choose significant discriminatory features for individual identification and consequently, outperform certain state-of-the-art methods in terms of recognition performance. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
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    Human identification system using 3D skeleton-based gait features and LSTM model
    (Academic Press Inc., 2022) Rashmi, M.; Guddeti, R.M.R.
    Vision-based gait emerged as the preferred biometric in smart surveillance systems due to its unobtrusive nature. Recent advancements in low-cost depth sensors resulted in numerous 3D skeleton-based gait analysis techniques. For spatial–temporal analysis, existing state-of-the-art algorithms use frame-level information as the timestamp. This paper proposes gait event-level spatial–temporal features and LSTM-based deep learning model that treats each gait event as a timestamp to identify individuals from walking patterns observed in single and multi-view scenarios. On four publicly available datasets, the proposed system stands superior to state-of-the-art approaches utilizing a variety of conventional benchmark protocols. The proposed system achieved a recognition rate of greater than 99% in low-level ranks during the CMC test, making it suitable for practical applications. The statistical study of gait event-level features demonstrated retrieved features’ discriminating capacity in classification. Additionally, the ANOVA test performed on findings from K folds demonstrated the proposed system's significance in human identification. © 2021 Elsevier Inc.
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    A secure fingerprint template generation mechanism using visual secret sharing with inverse halftoning
    (Academic Press Inc., 2023) Muhammed, A.; Pais, A.R.
    Fingerprints are the most popular and widely practiced biometric trait for human recognition and authentication. Due to the wide approval, reliable fingerprint template generation and secure saving of the generated templates are highly vital. Since fingers are permanently connected to the human body, loss of fingerprint data is irreversible. Cancelable fingerprint templates are used to overcome this problem. This paper introduces a novel cancelable fingerprint template generation mechanism using Visual Secret Sharing (VSS), data embedding, inverse halftoning, and super-resolution. During the fingerprint template generation, VSS shares with some hidden information are formulated as the secure cancelable template. Before authentication, the secret fingerprint image is reconstructed back from the VSS shares. The experimental results show that the proposed cancelable templates are simple, secure, and fulfill all the properties of the ideal cancelable templates, such as security, accuracy, non-invertibility, diversity, and revocability. The experimental analysis shows that the reconstructed fingerprint images are similar to the original fingerprints in terms of visual parameters and matching error rates. © 2023 Elsevier Inc.