User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network

Cao, Qian; Fei, Xu; Li, Huiyong

doi:10.3390/math10132283

Cited by 7 publications

(11 citation statements)

References 37 publications

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“…In Table V, results with the MMUISD and BB-MAS datasets have been compared with recent reviewed studies utilizing datasets of similar participant size [10,11,12,13,14]. The comparative studies utilized well-known public datasets such as IDNet and WhuGait with various LSTM models.…”

Section: Bb-mas Results Are Shown In Tablementioning

confidence: 99%

“…The IDNet dataset consists of accelerometer and gyroscope data collected from 50 subjects over a six-month period and was collected to classify gait cycles regardless of device orientation. Of the reviewed studies, [10], [11], and [12] used the IDNet dataset to evaluate various LSTM-based models and resulted in accuracy metrics ranging from 96-99%. Another notable dataset would be the WhuGait dataset, published in 2020 [13].…”

Section: Background and Related Workmentioning

confidence: 99%

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Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Kokal,

Vanamala,

Dave

2024

IJACSA

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In recent decades, mobile devices have evolved in potential and prevalence significantly while advancements in security have stagnated. As smartphones now hold unprecedented amounts of sensitive data, there is an increasing need to resolve this gap in security. To address this issue, researchers have experimented with biometric-based authentication methods to improve smartphone security. Following a comprehensive review, it was found that gait-based mobile authentication is under-researched compared to other behavioral biometrics. This study aims to contribute to the knowledge of biometric and gait-based authentication through the analysis of recent gait datasets and their potential with machine learning algorithms. Two recently published gait datasets were used with algorithms such as Random Forest, Decision Tree, and XGBoost to successfully differentiate users based on their respective walking features. Throughout this paper, the datasets, methodology, algorithms, experimental results, and goals for future work will be described.

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Section: Bb-mas Results Are Shown In Tablementioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Kokal,

Vanamala,

Dave

2024

IJACSA

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“…Cao et al [16], Y. Wang et al [17], X. Zeng et al [18], and Q. Zou et al [19] utilize Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) algorithms for mobile gait recognition using smartphone sensors. These systems collect data using an accelerometer [16], or an accelerometer and a gyroscope [17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

Edrah,

Ouda

2024

Computers

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With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone’s accelerometer sensor, along with advanced statistical methods, to detect the user’s gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature’s probability distribution function (PDF) is computed and compared to the known user’s PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold.

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“…The first input was the RGB frames, the second was the optical flow, and the third was the combination of both RGB frames and optical flow. The hybrid deep learning network (HDLN) proposed in [ 16 ] has been used to extract the features from complex smartphone inertial data. Deep learning models can also be used to automatically recognize the activity of a single worker.…”

Section: Related Workmentioning

confidence: 99%

KNN-Based Machine Learning Classifier Used on Deep Learned Spatial Motion Features for Human Action Recognition

Paramasivam¹,

Sindha

Sarojini

2023

Entropy

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Human action recognition is an essential process in surveillance video analysis, which is used to understand the behavior of people to ensure safety. Most of the existing methods for HAR use computationally heavy networks such as 3D CNN and two-stream networks. To alleviate the challenges in the implementation and training of 3D deep learning networks, which have more parameters, a customized lightweight directed acyclic graph-based residual 2D CNN with fewer parameters was designed from scratch and named HARNet. A novel pipeline for the construction of spatial motion data from raw video input is presented for the latent representation learning of human actions. The constructed input is fed to the network for simultaneous operation over spatial and motion information in a single stream, and the latent representation learned at the fully connected layer is extracted and fed to the conventional machine learning classifiers for action recognition. The proposed work was empirically verified, and the experimental results were compared with those for existing methods. The results show that the proposed method outperforms state-of-the-art (SOTA) methods with a percentage improvement of 2.75% on UCF101, 10.94% on HMDB51, and 0.18% on the KTH dataset.

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User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network

Cited by 7 publications

References 37 publications

Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

KNN-Based Machine Learning Classifier Used on Deep Learned Spatial Motion Features for Human Action Recognition

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