Smart Surveillance System by Face Recognition and Tracking Using Machine Learning Techniques

Niranjan, D. K.; Rakesh, Nitin

doi:10.1007/978-981-33-6862-0_1

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Cited by 5 publications

References 9 publications

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Enhancing Child Safety and Security Through Integrated Technology for Urban Areas

Narayan,

Sarang,

Viswajith

et al. 2024

2024 IEEE Global Humanitarian Technology Conference (GHTC)

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Enhancing Child Safety and Security Through Integrated Technology for Urban Areas

Narayan,

Sarang,

Viswajith

et al. 2024

2024 IEEE Global Humanitarian Technology Conference (GHTC)

View full text Add to dashboard Cite

Efficient Airlift and Enhanced Care: Unmanned Aerial Vehicles and Droid Solutions for Battlefield Casualty Evacuation

George,

Jose

et al. 2024

2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)

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Deep Learning-Driven Real-Time Facial Expression Tracking and Analysis in Virtual Reality

Liu

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, we use VR equipment to collect relevant facial expression images and normalize the angle, scale, and gray scale of the collected images. The direction quantization of image features is realized by 3D gradient computation, and then the histogram of the direction gradient of each video sub-block is cascaded into the final HOG3D descriptor so as to complete the extraction of dynamic expression features. In view of the multi-dimensional problem of the features, it is proposed to use a principal component analysis algorithm to reduce their dimensionality and use a multi-layer perceptron and deep confidence network to jointly construct the facial expression tracking recognition model. The datasets are used to analyze real-time facial expression tracking in virtual reality. The results present that the verification correctness of both datasets A and B reaches the maximum at the 120th iteration. In contrast, the loss value reaches the equilibrium state quickly at the 40th iteration. The dynamic occlusion expression recognition rate of the deep confidence network on dataset A (66.52%) is higher than that of the CNN (62.74%), which fully demonstrates that the method of this paper is able to effectively improve the performance of real-time facial expression tracking performance in virtual reality. This study can help computers further understand human emotions through facial expressions, which is of great significance to the development of the human-computer interaction field.

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Smart Surveillance System by Face Recognition and Tracking Using Machine Learning Techniques

Cited by 5 publications

References 9 publications

Enhancing Child Safety and Security Through Integrated Technology for Urban Areas

Enhancing Child Safety and Security Through Integrated Technology for Urban Areas

Efficient Airlift and Enhanced Care: Unmanned Aerial Vehicles and Droid Solutions for Battlefield Casualty Evacuation

Deep Learning-Driven Real-Time Facial Expression Tracking and Analysis in Virtual Reality

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