Deep neural network and 3D model for face recognition with multiple disturbing environments

Soni, Neha; Sharma, Enakshi K.; Kapoor, Amita

doi:10.1007/s11042-022-12698-2

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…Soni et al 27 proposed face recognition software based on a bird search-based shuffled Shepherd optimization (BSSSO) method. The suggested BSSSO technique employs a deep learning algorithm known as the CNN approach to improve the effectiveness and practicality of the recognition procedure.…”

Section: Related Workmentioning

confidence: 99%

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Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

Wang

Zhang

2023

J. Electron. Imag.

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Workflows can be optimized with the use of three-dimensional (3D) computer vision (CV) by monitoring processes from many angles. The potential for increased efficiency and cost savings is clear here. Input scenarios are always being monitored by security and surveillance systems to recognize and classify items, human faces, and moving things. The input reconstruction in these surveillance systems is problematic because of the mapping from three dimensions to two dimensions in the form of pixels. We present a new 3D CV paradigm-based framework for facial recognition and reconstruction. In this setup, deep neural networks are used to distinguish between mapped and unmapped pixels during a 2D to 3D or 3D to 2D transformation. To use correlation analysis to recognize human faces, it is necessary to first examine the input image for textural properties. The mapping procedure continues with the extraction of textural elements using dimensional contours. Then, continuous contours are used to do the 3D mapping and reduce the number of false positives. Instead, this framework isolates individual contours of moving objects or faces to increase the number of training iterations. Finally, missing pixels in the dimension conversion are filled to rebuild the human face utilizing discrete and continuous outlines. Hence, the suggested framework minimizes the false rate (10%), increases the false positive rate as (13.93%), and minimizes the error by (9.89%) while maximizing the recognition accuracy for (13.93%) and the precision is (13.21%).

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Section: Related Workmentioning

confidence: 99%

“…Soni et al 27 . proposed face recognition software based on a bird search-based shuffled Shepherd optimization (BSSSO) method.…”

Section: Related Workmentioning

confidence: 99%

Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

Wang

Zhang

2023

J. Electron. Imag.

View full text Add to dashboard Cite

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“…Singh et al put forward a new and robust description of color texture, thus emphasizing the advantages of related color www.ijacsa.thesai.org models in identifying color components, so as to help improve the accuracy of color face recognition [13]. Soni et al proposed a hybrid optimization algorithm based on bird search to remove image noise and improve face recognition accuracy [14]. Sharma et al designed a face recognition system based on field programmable gate array to improve the security of face recognition [15], aiming at the problem of imperfect user data protection at present.…”

Section: Related Workmentioning

confidence: 99%

Research on Image Algorithm for Face Recognition Based on Deep Learning

2023

IJACSA

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As people's requirements for applications are getting higher and higher, the recognition of facial features has been paid more and more attention. The current facial feature recognition algorithm not only takes a long time, but also has problems such as large system resource consumption and long running time in practical applications. Based on this, the research proposes a multi-task face recognition algorithm by combining multi-task deep learning on the basis of convolutional neural network, and analyzes its performance in four dimensions of face identity, age, gender, and fatigue state. The experimental results show that the multi-task face recognition algorithm model obtained through layer-by-layer progression takes less time than other models and can complete more tasks in the same training time. At the same time, comparing the best model M44 with other algorithms in four dimensions, it is found that the Mean Absolute Error lowest is 3.53, and the highest Accuracy value is 98.3%. On the whole, the multi-task face recognition algorithm proposed in the study can recognize facial features efficiently and quickly. At the same time, its training time is short, the calculation speed is fast, and the recognition accuracy is much higher than other algorithms. It is applied to intelligent driving behavior. Analysis, intelligent clothing navigation and other aspects have strong practical significance.

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Deep neural network and 3D model for face recognition with multiple disturbing environments

Cited by 2 publications

References 26 publications

Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

Research on Image Algorithm for Face Recognition Based on Deep Learning

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