Human Gait Recognition using Deep Learning: A Comprehensive Review

• URL: http://arxiv.org/abs/2309.10144v1
• Date: Mon, 18 Sep 2023 20:47:57 GMT
• Title: Human Gait Recognition using Deep Learning: A Comprehensive Review
• Authors: Muhammad Imran Sharif, Mehwish Mehmood, Muhammad Irfan Sharif and Md Palash Uddin
• Abstract summary: Gait recognition (GR) is a growing biometric modality used for person identification from a distance through visual cameras. GR provides a secure and reliable alternative to fingerprint and face recognition, as it is harder to distinguish between false and authentic signals. As video surveillance becomes more prevalent, new obstacles arise, such as ensuring uniform performance evaluation across different protocols, reliable recognition despite shifting lighting conditions, fluctuations in gait patterns, and protecting privacy.
• Score: 1.6085408991305155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gait recognition (GR) is a growing biometric modality used for person identification from a distance through visual cameras. GR provides a secure and reliable alternative to fingerprint and face recognition, as it is harder to distinguish between false and authentic signals. Furthermore, its resistance to spoofing makes GR suitable for all types of environments. With the rise of deep learning, steadily improving strides have been made in GR technology with promising results in various contexts. As video surveillance becomes more prevalent, new obstacles arise, such as ensuring uniform performance evaluation across different protocols, reliable recognition despite shifting lighting conditions, fluctuations in gait patterns, and protecting privacy.This survey aims to give an overview of GR and analyze the environmental elements and complications that could affect it in comparison to other biometric recognition systems. The primary goal is to examine the existing deep learning (DL) techniques employed for human GR that may generate new research opportunities.

Related papers

• A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities [1.6144710323800757]
  Hand Gesture Recognition (HGR) systems enhance natural, efficient, and authentic human-computer interaction. Despite significant progress, automatic and precise identification of hand gestures remains a considerable challenge in computer vision. This paper provides a comprehensive review of HGR techniques and data modalities from 2014 to 2024, exploring advancements in sensor technology and computer vision.
  arXiv  Detail & Related papers  (2024-08-10T04:40:01Z)
• ChatGPT Meets Iris Biometrics [10.902536447343465]
  This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition. We investigate how well AI tools like ChatGPT can understand and analyze iris images. Our findings suggest a promising path for future research and the development of more adaptable, efficient, robust and interactive biometric security solutions.
  arXiv  Detail & Related papers  (2024-08-09T05:13:07Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• Analysis of Recent Trends in Face Recognition Systems [0.0]
  Due to inter-class similarities and intra-class variations, face recognition systems generate false match and false non-match errors respectively. Recent research focuses on improving the robustness of extracted features and the pre-processing algorithms to enhance recognition accuracy.
  arXiv  Detail & Related papers  (2023-04-23T18:55:45Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)
• Multi-Modal Human Authentication Using Silhouettes, Gait and RGB [59.46083527510924]
  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.
  arXiv  Detail & Related papers  (2022-10-08T15:17:32Z)
• A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges [18.08349977960643]
  Gait recognition aims to identify a person at a distance, serving as a promising solution for long-distance and less-cooperation pedestrian recognition. Recent advancements in gait recognition have achieved inspiring success in many challenging scenarios by utilizing deep learning techniques. New challenges for gait recognition, including robust deep representation modeling, in-the-wild gait recognition, and recognition from new visual sensors such as infrared and depth cameras.
  arXiv  Detail & Related papers  (2022-06-28T03:36:12Z)
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [79.48829365560788]
  Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios. HG embedding aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks.
  arXiv  Detail & Related papers  (2020-11-30T15:03:47Z)
• Vision-based techniques for gait recognition [6.817301687702869]
  We discuss current techniques of gait recognition and modelling with the environment in which the research was conducted. We provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.
  arXiv  Detail & Related papers  (2020-04-30T06:33:01Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

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.