Two-Stage Multi-View Gait Recognition with a Re-Embedding Network

arXiv:2609.32244 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

License: http://creativecommons.org/licenses/by/4.0/

The gist: Gait recognition always remains challenging due to severe overfitting and the rigid view constraints common in single-stage approaches.

Terminology

Abstract

Gait recognition always remains challenging due to severe overfitting and the rigid view constraints common in single-stage approaches. We propose a two-stage framework, termed Translate-First-Then-Reason (TFTR), to address these issues. In the first stage, a shallow Siamese convolutional network with triplet loss maps Gait Energy Images (GEIs) into a 128-dimensional view-specific embedding space. In the second stage, these per-view embeddings are treated as tokens and processed by a 12-layer Transformer encoder, which re-projects them into a new space with improved cosine separability. This design enables flexible fusion of an arbitrary number of views at inference, overcoming the fixed-input limitations of prior methods. Trained on the OU-MVLP dataset (6,000 subjects) and evaluated on unseen CASIA-B across normal, bag-carrying, and coat-wearing conditions, our pipeline achieves 96.91% single-view and 99.49% three-view accuracy on OU-MVLP, and attains 100% accuracy on CASIA-B with three views.

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