You Can Run but not Hide: Improving Gait Recognition with Intrinsic Occlusion Type Awareness

• URL: http://arxiv.org/abs/2312.02290v1
• Date: Mon, 4 Dec 2023 19:11:40 GMT
• Title: You Can Run but not Hide: Improving Gait Recognition with Intrinsic Occlusion Type Awareness
• Authors: Ayush Gupta, Rama Chellappa
• Abstract summary: Occluded body parts can affect gait recognition from uncontrolled outdoor sequences at range. Most current methods assume the availability of complete body information while extracting the gait features. We propose an occlusion aware gait recognition method which can be used to model intrinsic occlusion awareness into potentially any state-of-the-art gait recognition method.
• Score: 48.151855620080134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While gait recognition has seen many advances in recent years, the occlusion problem has largely been ignored. This problem is especially important for gait recognition from uncontrolled outdoor sequences at range - since any small obstruction can affect the recognition system. Most current methods assume the availability of complete body information while extracting the gait features. When parts of the body are occluded, these methods may hallucinate and output a corrupted gait signature as they try to look for body parts which are not present in the input at all. To address this, we exploit the learned occlusion type while extracting identity features from videos. Thus, in this work, we propose an occlusion aware gait recognition method which can be used to model intrinsic occlusion awareness into potentially any state-of-the-art gait recognition method. Our experiments on the challenging GREW and BRIAR datasets show that networks enhanced with this occlusion awareness perform better at recognition tasks than their counterparts trained on similar occlusions.

Related papers

• Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning [4.724899372568309]
  Gesture recognition is a foundational task in human-machine interaction. It is essential to effectively discern and reject unknown gestures of disinterest in a robust system. We propose a more effective PL method leveraging two novel and inherent distinctions, feature activation level and projection inconsistency.
  arXiv  Detail & Related papers  (2023-12-05T06:49:15Z)
• 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)
• Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning [59.548960057358435]
  We propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities. Our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities.
  arXiv  Detail & Related papers  (2022-07-08T09:23:59Z)
• 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)
• RealGait: Gait Recognition for Person Re-Identification [79.67088297584762]
  We construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner. Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.
  arXiv  Detail & Related papers  (2022-01-13T06:30:56Z)
• Gait Recognition Based on Deep Learning: A Survey [1.731119436110423]
  Biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Some works suggest addressing the problem through gait recognition approaches. Deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision related problem.
  arXiv  Detail & Related papers  (2022-01-10T12:44:42Z)
• Harnessing Unrecognizable Faces for Face Recognition [87.80037162457427]
  We propose a measure of recognizability of a face image, implemented by a deep neural network trained using mostly recognizable identities. We show that accounting for recognizability reduces error rate of single-image face recognition by 58% at FAR=1e-5.
  arXiv  Detail & Related papers  (2021-06-08T05:25:03Z)
• Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction [53.03469655641418]
  We present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. We establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases.
  arXiv  Detail & Related papers  (2021-05-18T20:47:45Z)
• Preserving Privacy in Human-Motion Affect Recognition [4.753703852165805]
  This work evaluates the effectiveness of existing methods at recognising emotions using both 3D temporal joint signals and manually extracted features. We propose a cross-subject transfer learning technique for training a multi-encoder autoencoder deep neural network to learn disentangled latent representations of human motion features.
  arXiv  Detail & Related papers  (2021-05-09T15:26:21Z)
• Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition [2.492343817244558]
  Self attention based approach is proposed for wearable sensor based human activity recognition. It incorporates self-attention based feature representations from encoder to detect unseen activity classes in open-set recognition setting. We conduct extensive validation experiments that indicate significantly improved robustness to noise and subject specific variability in body-worn sensor signals.
  arXiv  Detail & Related papers  (2021-03-07T06:21:18Z)

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.