A novel texture-based handcraft feature: spatial aggregated LD2oDP-VLAD for gait-based person recognition

Ritu Singh; Chiranjit Dutta; Pradeep Kumar Singh

doi:10.2478/ijssis-2025-0054

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A novel texture-based handcraft feature: spatial aggregated LD2oDP-VLAD for gait-based person recognition

International Journal on Smart Sensing and Intelligent Systems

Volume 19 (2026): Issue 1 (January 2026)

By: Ritu Singh, Chiranjit Dutta and Pradeep Kumar Singh

Open Access

|Aug 2026

Abstract

Gait recognition is becoming a biometric approach due to its passive acquisition and applicability in security and surveillance scenarios. However, existing handcrafted descriptors often fail to capture fine-grained variations and are sensitive to noise, limiting recognition performance. This work proposes a novel Local Directional Second-Order Derivative Pattern (LD2oDP) descriptor to encode sharper local intensity variations and preserve structural edge information. The extracted local features are aggregated using a Spatial Pyramid VLAD (SP-VLAD) framework to form a discriminative global representation. Experiments on the CASIA-B and CASIA-C datasets demonstrate improved recognition accuracy under viewpoint, clothing, carrying, and walking-speed variations.

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Articles in this issue

DOI: https://doi.org/10.2478/ijssis-2025-0054 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Nov 30, 2025

Published on: Aug 3, 2026

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

gait,

GEI,

Related subjects:

Introductions and overviews,

Engineering, other

© 2026 Ritu Singh, Chiranjit Dutta, Pradeep Kumar Singh, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2026): Issue 1 (January 2026)