Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition

The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.

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Bayesian Image Restoration Using a Large-Scale Total Patch Variation Prior

Mathematical Problems in Engineering ◽

10.1155/2011/408241 ◽

2011 ◽

Vol 2011 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Yang Chen ◽

Weimin Yu ◽

Yinsheng Li ◽

Zhou Yang ◽

Limin Luo ◽

...

Keyword(s):

Image Restoration ◽

Large Scale ◽

Computational Cost ◽

Real Data ◽

Inverse Image ◽

Edge Preserving ◽

Device Architecture ◽

Mixture Prior ◽

Ill Posed ◽

Improved Performance

Edge-preserving Bayesian restorations using nonquadratic priors are often inefficient in restoring continuous variations and tend to produce block artifacts around edges in ill-posed inverse image restorations. To overcome this, we have proposed a spatial adaptive (SA) prior with improved performance. However, this SA prior restoration suffers from high computational cost and the unguaranteed convergence problem. Concerning these issues, this paper proposes a Large-scale Total Patch Variation (LS-TPV) Prior model for Bayesian image restoration. In this model, the prior for each pixel is defined as a singleton conditional probability, which is in a mixture prior form of one patch similarity prior and one weight entropy prior. A joint MAP estimation is thus built to ensure the iteration monotonicity. The intensive calculation of patch distances is greatly alleviated by the parallelization of Compute Unified Device Architecture(CUDA). Experiments with both simulated and real data validate the good performance of the proposed restoration.

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Gait recognition for human identification using ensemble of LVQ Neural Networks

2012 International Conference on Biomedical Engineering (ICoBE) ◽

10.1109/icobe.2012.6179001 ◽

2012 ◽

Cited By ~ 1

Author(s):

Neda Kordjazi ◽

Saeid Rahati

Keyword(s):

Neural Networks ◽

Gait Recognition ◽

Human Identification

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Gait Recognition and Analysis

Behavioral Biometrics for Human Identification ◽

10.4018/978-1-60566-725-6.ch007 ◽

2010 ◽

pp. 151-168

Author(s):

Shiqi Yu ◽

Liang Wang

Keyword(s):

Computer Vision ◽

Feature Extraction ◽

Gait Recognition ◽

Human Identification ◽

Visual Surveillance ◽

Evaluation Metrics ◽

Surveillance Systems ◽

Research Challenges ◽

Recent Advances ◽

Biometric Feature

With the increasing demands of visual surveillance systems, human identification at a distance is an urgent need. Gait is an attractive biometric feature for human identification at a distance, and recently has gained much interest from computer vision researchers. This chapter provides a survey of recent advances in gait recognition. First, an overview on gait recognition framework, feature extraction, and classifiers is given, and then some gait databases and evaluation metrics are introduced. Finally, research challenges and applications are discussed in detail.

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A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks

Electronics ◽

10.3390/electronics10212657 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2657

Author(s):

Jibin Yin ◽

Pengfei Zhao ◽

Yi Zhang ◽

Yi Han ◽

Shuoyu Wang

Keyword(s):

Neural Networks ◽

Large Scale ◽

Deep Neural Networks ◽

Data Augmentation ◽

Real Data ◽

War Trauma ◽

Trauma Severity ◽

Large Scale Analysis ◽

Medical Evaluation ◽

Modern Warfare

The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed. First, the proposed WTSS, which uses multiple non-linear regression based on the characteristics of war trauma data and the medical evaluation by an expert panel, performed a standardized assessment of an injury and predicts its trauma consequences. Second, to generate virtual injury, based on the probability of occurrence, the injured parts, injury types, and complications were randomly sampled and combined, and then WTSS was used to assess the consequences of the virtual injury. Third, to evaluate the accuracy of the predicted injury consequences, we built a DNN classifier and then trained it with the generated data and tested it with real data. Finally, we used the Delphi method to filter out unreasonable injuries and improve data rationality. The experimental results verified that the proposed approach surpassed the traditional artificial generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war trauma data augmentation.

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Performance of Convolutional Neural Networks for Human Identification by Gait Recognition

Journal of Artificial Intelligence ◽

10.3923/jai.2018.30.38 ◽

2017 ◽

Vol 11 (1) ◽

pp. 30-38

Author(s):

Mohamed Sayed

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Gait Recognition ◽

Human Identification

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Deep supervised hashing for gait retrieval

F1000Research ◽

10.12688/f1000research.51368.1 ◽

2021 ◽

Vol 10 ◽

pp. 1038

Author(s):

Shohel Sayeed ◽

Pa Pa Min ◽

Thian Song Ong

Keyword(s):

Loss Function ◽

Large Scale ◽

Promising Result ◽

Gait Recognition ◽

Feature Representation ◽

Surveillance Systems ◽

Convolutional Network ◽

Gait Features ◽

Retrieval Problem ◽

Hash Codes

Background: Gait recognition is perceived as the most promising biometric approach for future decades especially because of its efficient applicability in surveillance systems. Due to recent growth in the use of gait biometrics across surveillance systems, the ability to rapidly search for the required data has become an emerging need. Therefore, we addressed the gait retrieval problem, which retrieves people with gaits similar to a query subject from a large-scale dataset. Methods: This paper presents the deep gait retrieval hashing (DGRH) model to address the gait retrieval problem for large-scale datasets. Our proposed method is based on a supervised hashing method with a deep convolutional network. We use the ability of the convolutional neural network (CNN) to capture the semantic gait features for feature representation and learn the compact hash codes with the compatible hash function. Therefore, our DGRH model combines gait feature learning with binary hash codes. In addition, the learning loss is designed with a classification loss function that learns to preserve similarity and a quantization loss function that controls the quality of the hash codes Results: The proposed method was evaluated against the CASIA-B, OUISIR-LP, and OUISIR-MVLP benchmark datasets and received the promising result for gait retrieval tasks. Conclusions: The end-to-end deep supervised hashing model is able to learn discriminative gait features and is efficient in terms of the storage memory and speed for gait retrieval.

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