Multi-Modal Human Authentication Using Silhouettes, Gait and RGB

• URL: http://arxiv.org/abs/2210.04050v1
• Date: Sat, 8 Oct 2022 15:17:32 GMT
• Title: Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
• Authors: Yuxiang Guo, Cheng Peng, Chun Pong Lau, Rama Chellappa
• Abstract summary: Whole-body-based human authentication is a promising approach for remote biometrics scenarios. We propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis.
• Score: 59.46083527510924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their advantages and drawbacks. In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis. The GaitPattern contributes to robust identification performance over a large range of viewing angles. Extensive experimental results on the CASIA-B dataset demonstrate that the proposed method outperforms state-of-the-art recognition systems. We also provide experimental results using the newly collected BRIAR dataset.

Related papers

• Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis [48.59382455101753]
  2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining.
  arXiv  Detail & Related papers  (2024-03-11T09:12:24Z)
• Distillation-guided Representation Learning for Unconstrained Gait Recognition [50.0533243584942]
  We propose a framework, termed GAit DEtection and Recognition (GADER), for human authentication in challenging outdoor scenarios. GADER builds discriminative features through a novel gait recognition method, where only frames containing gait information are used. We evaluate our method on multiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets.
  arXiv  Detail & Related papers  (2023-07-27T01:53:57Z)
• Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline [95.88825497452716]
  Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. GREW is the first large-scale dataset for gait recognition in the wild. SPOSGait is the first NAS-based gait recognition model.
  arXiv  Detail & Related papers  (2022-05-05T14:57:39Z)
• Towards a Deeper Understanding of Skeleton-based Gait Recognition [4.812321790984493]
  In recent years, most gait recognition methods used the person's silhouette to extract the gait features. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks.
  arXiv  Detail & Related papers  (2022-04-16T18:23:37Z)
• Model-based gait recognition using graph network on very large population database [3.8707695363745223]
  In this paper, to resist the increase of subjects and views variation, local features are built and a siamese network is proposed. Experiments on the very large population dataset named OUM-Pose and the popular dataset, CASIA-B, show that our method archives some state-of-the-art (SOTA) performances in model-based gait recognition.
  arXiv  Detail & Related papers  (2021-12-20T02:28:02Z)
• View-Invariant Gait Recognition with Attentive Recurrent Learning of Partial Representations [27.33579145744285]
  We propose a network that first learns to extract gait convolutional energy maps (GCEM) from frame-level convolutional features. It then adopts a bidirectional neural network to learn from split bins of the GCEM, thus exploiting the relations between learned partial recurrent representations. Our proposed model has been extensively tested on two large-scale CASIA-B and OU-M gait datasets.
  arXiv  Detail & Related papers  (2020-10-18T20:20:43Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• Creating Artificial Modalities to Solve RGB Liveness [79.9255035557979]
  We introduce two types of artificial transforms: rank pooling and optical flow, combined in end-to-end pipeline for spoof detection. The proposed method achieves state-of-the-art on the largest cross-ethnicity face anti-spoofing dataset CASIA-SURF CeFA (RGB)
  arXiv  Detail & Related papers  (2020-06-29T13:19:22Z)
• Skeleton Focused Human Activity Recognition in RGB Video [11.521107108725188]
  We propose a multimodal feature fusion model that utilizes both skeleton and RGB modalities to infer human activity. The model could be either individually or uniformly trained by the back-propagation algorithm in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-04-29T06:40:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.