Gait Recognition with Temporal Kolmogorov-Arnold Networks

Mohammed Asad; Dinesh Kumar Vishwakarma

cs.CVApr 11, 2026

Gait Recognition with Temporal Kolmogorov-Arnold Networks

Authors: Mohammed Asad, Dinesh Kumar Vishwakarma

Organizations: Multi-Modal Data Analytics Lab, Department of Information Technology, Delhi Technological University, Delhi, 110042, India

Abstract

Gait recognition is a biometric modality that identifies individuals from their characteristic walking patterns. Unlike conventional biometric traits, gait can be acquired at a distance and without active subject cooperation, making it suitable for surveillance and public safety applications. Nevertheless, silhouette-based temporal models remain sensitive to long sequences, observation noise, and appearance-related covariates. Recurrent architectures often struggle to preserve information from earlier frames and are inherently sequential to optimize, whereas transformer-based models typically require greater computational resources and larger training sets and may be sensitive to irregular sequence lengths and noisy inputs. These limitations reduce robustness under clothing variation, carrying conditions, and view changes, while also hindering the joint modeling of local gait cycles and longer-term motion trends. To address these challenges, we introduce a Temporal Kolmogorov-Arnold Network (TKAN) for gait recognition. The proposed model replaces fixed edge weights with learnable one-dimensional functions and incorporates a two-level memory mechanism consisting of short-term RKAN sublayers and a gated long-term pathway. This design enables efficient modeling of both cycle-level dynamics and broader temporal context while maintaining a compact backbone. Experiments on the CASIA-B dataset indicate that the proposed CNN+TKAN framework achieves strong recognition performance under the reported evaluation setting.

Figures & tables

Figure 1: Proposed pipeline. A shared CNN encodes each silhouette frame into a compact feature; the frame features are stacked and batch-normalized to form a sequence. The TKAN head models short- and long-range gait dynamics over the sequence and outputs refined time-step features, which are temporally averaged (with dropout) to obtain a clip-level descriptor. A linear–softmax classifier maps this descriptor to the identity posterior and the top-1 label is reported. (In our setup: $T{=}50$ , feature width $d{=}256$ , classes $C{=}74$ .)

Figure 2: Architecture of CNN model

Figure 3: Training/validation loss and accuracy for CNN+TKAN on CASIA–B (subject split 1–74 train, 75–124 test).

Method	NM	BG	CL	Avg
CNN+TKAN (Ours)	99.52	99.56	98.82	99.30
VPNet (CVPR’24) [ 12 ]	98.3	96.3	90.0	94.9
CLTD (ECCV’24) [ 13 ]	98.6	96.4	89.3	94.8
HSTL (ICCV’23) [ 14 ]	98.1	95.9	88.9	94.3
GaitGCI (CVPR’23) [ 15 ]	98.4	96.6	88.5	94.5
DyGait (ICCV’23) [ 16 ]	98.4	96.2	87.8	94.1

Table 1: Comparison on CASIA-B (Rank-1 %) under the standard protocol (subjects 1–74 train, 75–124 test; identical-view excluded). † indicates enlarged resolution (e.g., 128

\times

88).

Method	NM	BG	CL
CNN + LSTM	98.06 / 99.63	96.76 / 99.56	92.56 / 98.90
CNN + Transformer	98.86 / 99.82	97.50 / 99.85	95.21 / 98.45
CNN + TKAN (ours)	99.52 / 100.00	99.56 / 100.00	98.82 / 100.00

Table 2: Internal comparison (same CNN, training, and protocol). Rank-1 / Rank-5 (%) on CASIA-B by condition.

Panel of Figure 4 — Figure 4: ROC and AUC for CNN+TKAN on CASIA–B, showing both micro- and macro-averaged one-vs-rest ROC curves, reported separately for NM, BG, and CL.

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Local Spatiotemporal Convolutional Network for Robust Gait Recognition

Xiaoyun Wang, Cunrong Li, Wu Wang

School of Mechanical and Electrical Engineering, Osh State University

Convolutional Neural Networks Gait Recognition

Open paper AskPDF arXiv

Gait recognition, as a promising biometric technology, identifies individuals through their unique walking patterns and offers distinctive advantages including non-invasiveness, long-range applicability, and resistance to deliberate disguise. Despite these merits, capturing the intrinsic motion patterns concealed within consecutive video frames remains challenging due to the complexity of video data and the interference of external covariates such as viewpoint changes, clothing variations, and carrying conditions. Existing approaches predominantly rely on either static appearance features extracted from individual silhouette frames or employ complex sequential models (\eg, LSTM, 3D convolutions) that demand substantial computational resources and sophisticated training strategies. To address these limitations, we propose a Local Spatiotemporal Convolutional Network (LSTCN), a structurally simple yet highly effective dual-branch architecture that endows standard two-dimensional convolutional networks with the capacity to extract temporal information. Specifically, we introduce a Global Bidirectional Spatial Pooling (GBSP) mechanism that reduces the dimensionality of gait tensors by decomposing spatial features into horizontal and vertical strip-based local representations, enabling the temporal dimension to participate in standard 2D convolution operations. Building upon this, we design a Local Spatiotemporal Convolutional (LSTC) layer that jointly processes temporal and spatial dimensions, allowing the network to adaptively learn strip-based gait motion patterns. We further extend this formulation with asymmetric convolution kernels that independently attend to the temporal, spatial, and joint spatiotemporal domains, thereby enriching the extracted feature representations.

Abstract

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