Extending Appearance Based Gait Recognition with Depth Data

Lenac, Kristijan; Sušanj, Diego; Ramakić, Adnan; Pinčić, Domagoj

doi:10.3390/app9245529

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…The first step in our proposed approach is data preparation. In general, assuming the input data are in the form of raw RGB image sequences taken from a camera, the typical gait data preprocessing steps [ 12 , 27 ] are applied. First, the noise is filtered from the images.…”

Section: The Proposed Approachmentioning

confidence: 99%

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Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers

Pinčić

Sušanj

Lenac

2022

Sensors

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Gait is a unique biometric trait with several useful properties. It can be recognized remotely and without the cooperation of the individual, with low-resolution cameras, and it is difficult to obscure. Therefore, it is suitable for crime investigation, surveillance, and access control. Existing approaches for gait recognition generally belong to the supervised learning domain, where all samples in the dataset are annotated. In the real world, annotation is often expensive and time-consuming. Moreover, convolutional neural networks (CNNs) have dominated the field of gait recognition for many years and have been extensively researched, while other recent methods such as vision transformer (ViT) remain unexplored. In this manuscript, we propose a self-supervised learning (SSL) approach for pretraining the feature extractor using the DINO model to automatically learn useful gait features with the vision transformer architecture. The feature extractor is then used for extracting gait features on which the fully connected neural network classifier is trained using the supervised approach. Experiments on CASIA-B and OU-MVLP gait datasets show the effectiveness of the proposed approach.

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Section: The Proposed Approachmentioning

confidence: 99%

“…Chen et al [ 11 ] used CNN network for gait classification based on the multistatic micro-doppler signatures. A detailed description of a typical gait identification pipeline can be found in [ 3 , 7 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers

Pinčić

Sušanj

Lenac

2022

Sensors

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“…Ramakić et al [7] and Lenac et al [8] presented approaches where they used appearancebased methods, such as GEI and BGEI, and height feature obtained from depth images for gait recognition tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Lenac et al [8] presented HGEI-i and HGEI-f methods where in case of first method, HGEI-i, early fusion of information is realized while HGEI-f performs late fusion of information. HGEI-i represents a method for gait recognition where are combined GEI features and feature height of a person in a way that height of a person is added as one of a features alongside features obtained from GEI.…”

Section: Related Workmentioning

confidence: 99%

Feature extraction for person gait recognition applications

2021

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In this paper we present some features that may be used in person gait recognition applications. Gait recognition is an interesting way of people identification. During a gait cycle, each person creates unique patterns that can be used for people identification. Also, gait recognition methods ordinarily do not need interaction with a person and that is the main advantage of these methods. Features used in a person gait recognition methods can be obtained with widely available RGB and RGB-D cameras. In this paper we present a two features which are suitable for use in gait recognition applications. Mentioned features are height of a person and step length of a person. They may be extracted and were extracted from depth images obtained from RGB-D camera. For experimental purposes, we used a custom dataset created in outdoor environment using a long-range stereo camera.

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“…Studies on gait recognition began with the use of vision sensors [23]. These approaches were subsequently further studied and developed [24][25][26][27][28][29]. In general, vision-based gait recognition requires strict conditions while collecting the data.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Few-Shot Learning for Gait Recognition

Moon

Minaya

et al. 2020

Applied Sciences

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A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

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Extending Appearance Based Gait Recognition with Depth Data

Cited by 10 publications

References 25 publications

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers

Feature extraction for person gait recognition applications

Multimodal Few-Shot Learning for Gait Recognition

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