Optimizing Gait-Based Biometric Recognition with Deep Dense Networks

Sachin Mandlik; Rekha Labade; Sachin Chaudhari; Balasaheb Agarkar

doi:10.28951/bjb.v43i4.857

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Published: Nov 25, 2025

DOI: https://doi.org/10.28951/bjb.v43i4.857

Keywords:

Deep learning Pyramid mapping Spatial variations Time-based modulations Gait Recognition

Sachin Mandlik

Savitribai Phule Pune University

https://orcid.org/0000-0002-7097-8253

Rekha Labade

Savitribai Phule Pune University

Sachin Chaudhari

Savitribai Phule Pune University

Balasaheb Agarkar

Savitribai Phule Pune University

Abstract

Gait recognition is a developing biometric technique capable of identifying individuals from a distance, with wide-ranging applications such as video surveillance. A primary challenge is the extraction of discriminative gait features from silhouettes that are robust to variations in apparel, carried objects, and camera viewpoints. To address these limitations, this study introduces GaitSTR — a novel framework that harnesses pyramid mapping for enhanced temporal and spatial feature extraction, integrated with a deep neural network comprising dense layers. Pyramid mapping decomposes gait sequences into multi-scale spatial features, enabling GaitSTR to capture fine-to-coarse motion patterns and improve recognition under varying conditions. The method focuses on extracting distinctive feature representations at different frame levels, effectively utilizing spatial and temporal variations within video sequences. The proposed model utilizes a memory-augmented recurrent neural network (RNN) enriched with temporal attention to capture sequential motion cues, while spatial features are extracted through a densely connected attention-guided network By employing the pyramid-based hierarchical feature extraction, along with attention mechanisms in both spatial and temporal component, the network can prioritize the most significant video segments, improving its efficiency and learning capacity for processing intricate gait data. The results are evaluated on four widely used benchmark datasets: GREW, OU-ISIR, OU-MVLP, and CASIA-B—achieving 92.4% on GREW, 95.2% on OU-ISIR, and 0.96 mean accuracy on OU-MVLP, and 98.4% (normal) on CASIA-B, surpassing state-of-the-art methods. These results underscore the robustness of our approach under diverse conditions, establishing a new benchmark for performance in gait recognition.

How to Cite

Mandlik, S., Labade, R., Chaudhari, S., & Agarkar, B. (2025). Optimizing Gait-Based Biometric Recognition with Deep Dense Networks. Brazilian Journal of Biometrics, 43(4), e-43857. https://doi.org/10.28951/bjb.v43i4.857

Issue

Vol. 43 No. 4 (2025): Continuous Publication.

Section

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Author Biographies

Sachin Mandlik, Savitribai Phule Pune University

Sachin B. Mandlik earned his Bachelor of Engineering (B.E.) degree in Electronics Engineering from Pune University, Pune, in 2006. He later completed his Master of Engineering (M.E.) degree in Electronics Engineering from Jawaharlal Nehru Engineering College, Aurangabad, which is affiliated with Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, in 2012. Currently, he is pursuing his Ph.D. in the Department of Electronics and Telecommunication Engineering at Sanjivani College of Engineering, Kopargaon, affiliated with Savitribai Phule Pune University, Pune, India. His research focuses on areas such as pattern recognition, image processing, digital circuits, analog and digital communication systems, and power electronics. Mandlik’s work extends to applications like sensor-based control systems, automated packaging solutions, intelligent cargo management, digital ticketing systems, and robotics for safety applications. Additionally, he has contributed to research in advanced antenna positioning, intelligent robotics, and systems for crack detection.

Rekha Labade, Savitribai Phule Pune University

Dr. Rekha P. Labade earned her Ph.D. in Electronics and Telecommunication Engineering from Symbiosis International University, Pune. She serves as a Research Guide in the Department of Electronics and Telecommunication Engineering at Sanjivani College of Engineering, Kopargaon, affiliated with Savitribai Phule Pune University, Pune, India. Additionally, she holds the position of Professor at Amrutvahini College of Engineering, Sangamner. Dr. Labade is a passionate researcher with expertise in antenna technology, particularly in the design and analysis of microstrip antennas. Her research focuses on critical parameters such as dielectric constant, resonance frequency, return loss, and radiation patterns, which are essential for developing compact and high-performance antennas. These advancements are crucial for modern communication systems, including applications in Wi-Fi, Bluetooth, and 5G networks. With over 70 publications in esteemed international journals, Dr. Labade has made substantial contributions to her field. She is an active member of professional organizations such as The Institution of Electronics and Telecommunication Engineers (IETE) and The Institute of Electrical and Electronics Engineers (IEEE). Furthermore, she is an Executive Committee member of the IEEE Bombay Section.

Sachin Chaudhari, Savitribai Phule Pune University

Dr. Sachin V. Chaudhari holds a Ph.D. in Electronics Engineering and currently serves as an Associate Professor in the Department of Electronics and Telecommunication Engineering at Sanjivani College of Engineering, Kopargaon, affiliated with Savitribai Phule Pune University, Pune, India. His research spans a diverse range of areas, including automated deep learning, UAV route planning, hybrid energy systems, and medical applications. Dr. Chaudhari has also contributed to innovative studies on dynamic routing algorithms, channel estimation in OFDM systems, and the development of advanced materials for medical implants. With over eight publications in renowned international journals, he has demonstrated his expertise and commitment to advancing the field. Additionally, he is a professional member of The Institution of Engineers (IEI), reflecting his active engagement in the engineering community.

Balasaheb Agarkar, Savitribai Phule Pune University

Dr. Balasaheb S. Agarkar holds a Ph.D. in Electronics Engineering and currently serves as a Professor and Head of the Electronics and Telecommunication Engineering Department at Sanjivani College of Engineering, Kopargaon, affiliated with Savitribai Phule Pune University, Pune, India. His research encompasses a wide range of interdisciplinary areas, including Gesture Recognition, Human-Computer Interaction, and System Architecture. A significant focus of his work is on Hand Gesture Recognition, Hand Movements, and Hand Position, which play a pivotal role in advancing mobile phone technology and pedestrian interaction systems. Dr. Agarkar has also explored the use of innovative materials, such as Aluminum Foil, in system design, contributing to the development of cutting-edge technologies that enhance human-computer interaction and mobile communication. With over 12 publications in esteemed international journals, he has made notable contributions to his field. He is an active member of several professional organizations, including The Institution of Electronics and Telecommunication Engineers (IETE), The Institute of Electrical and Electronics Engineers (IEEE), and The Indian Society for Technical Education (ISTE).

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Sachin Mandlik, Savitribai Phule Pune University

Rekha Labade, Savitribai Phule Pune University

Sachin Chaudhari, Savitribai Phule Pune University

Balasaheb Agarkar, Savitribai Phule Pune University

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