Multi-Model Long Short-Term Memory Network for Gait Recognition Using Window-Based Data Segment

Tran, L.T.; Hoang, Thang Manh; Nguyen, Thuc D.; Kim, Hyunil; Choi, Deokjai

doi:10.1109/access.2021.3056880

Cited by 51 publications

(40 citation statements)

References 39 publications

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“…Recently, Tran et al [103] proposed an Inertial Measurement Units (IMUs)-based gait recognition approach. The authors employed LSTMs to exploit the temporal information on video sequences, thus extracting hidden patterns inside such sequences.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

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Gait Recognition Based on Deep Learning: A Survey

et al. 2022

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In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision-related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

“…A training method that relies on the differentiaton of an original input and a generate counterpart from a model, such as a CNN. [76,103,115,133,140]…”

Section: Generative Adversarial Network Generative Adversarial Networkmentioning

confidence: 99%

Gait Recognition Based on Deep Learning: A Survey

et al. 2022

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“…We use acceleration and gyroscope signals as the data source for key generation. The data preprocessing and segmentation methods are referred from [44], which are summarized as follows. [45], to overcome the asynchrony between the gyroscope and accelerometer.…”

Section: Data Preprocessing Blockmentioning

confidence: 99%

“…The overall network architecture comprises of two branches as Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) network, extracting the features independently (see Figure 2). The deep network for extracting deep features from gait segment, where CNN is from [46] and LSTM is from [44].…”

Section: Network Architecturementioning

confidence: 99%

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Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers

Tran,

Nguyen,

Kim

et al. 2021

Preprint

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Gait data captured by inertial sensors have demonstrated promising results on user authentication. However, most existing approaches stored the enrolled gait pattern insecurely for matching with the validating pattern, thus, posed critical security and privacy issues.In this study, we present a gait cryptosystem that generates from gait data the random key for user authentication, meanwhile, secures the gait pattern.First, we propose a revocable and random binary string extraction method using a deep neural network followed by feature-wise binarization. A novel loss function for network optimization is also designed, to tackle not only the intrauser stability but also the inter-user randomness. Second, we propose a new biometric key generation scheme, namely Irreversible Error Correct and Obfuscate (IECO), improved from the Error Correct and Obfuscate (ECO) scheme, to securely generate from the binary string the random and irreversible key. The model was evaluated with two benchmark datasets as OU-ISIR and whuGAIT.We showed that our model could generate the key of 139 bits from 5-second data sequence with zero False Acceptance Rate (FAR) and False Rejection Rate (FRR) smaller than 5.

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HECC‐ABE: A novel blockchain‐based IoT healthcare data storage using hybrid cryptography schemes with key optimization by hybrid meta‐heuristic algorithm

Dubey,

Ramanjaneyulu,

Saraswat

et al. 2023

Trans Emerging Tel Tech

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The most contemporary development in information technology is the Internet of Things (IoT), which integrates the digital world with the real world. It makes it possible for things and people to communicate via the Internet. Blockchain is one of the popular topics of interest right now, and it can be used in most IoT applications. The blockchain's salient characteristics, like decentralization, data integrity, confidentiality, protection, and openness, are the main reasons for implementing it in healthcare services. Hence, this paper introduces an effective approach for storing IoT‐based healthcare data storage in the blockchain. The procedures in this suggested paradigm are as follows: user authentication, user trust verification, and optimal key storage in the blockchain. Wearable sensors are inserted in or adhered to a patient's body and used by IoT networks to gather healthcare data. These details are supplied into the verification structure constructed by adaptive dilated long short term memory with attention network (AD‐LSTM‐AN). Once the user's authentication is confirmed, then the user's trust level is computed. The same AD‐LSTM‐AN‐based verification structure is used to verify the historical data that has been stored and retrieved by the user, together with their previous transactions, in this phase. The healthcare data are then sent to the hybrid elliptic curve cryptography with attribute‐based encryption (HECC‐ABE) cryptography schemes for data encryption once the authentication of the person who attempts to store the data is confirmed. An adaptive final position‐based golden eagle‐Harris hawks optimization (AFP‐GEHHO) algorithm is used for optimizing the keys of the encrypted data. The blockchain platform stores the encrypted data of the authorized user with a high level of trust. The same process is done in reverse order whenever a user needs to recover information that is saved in the blockchain to retrieve the initially stored healthcare data. The effectiveness of secure data storage in blockchain is tested through experimental simulations. Throughout the analysis, the designed model achieves 96% in terms of accuracy. Thus, the developed model shows more secure and effective, has less error and reduces the time and memory requirements compared to existing approaches.

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Multi-Model Long Short-Term Memory Network for Gait Recognition Using Window-Based Data Segment

Cited by 51 publications

References 39 publications

Gait Recognition Based on Deep Learning: A Survey

Gait Recognition Based on Deep Learning: A Survey

Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers

HECC‐ABE: A novel blockchain‐based IoT healthcare data storage using hybrid cryptography schemes with key optimization by hybrid meta‐heuristic algorithm

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