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Article

An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

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Department of Computer Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), Charotar University of Science and Technology (CHARUSAT), Nadiad-Petlad Road, Highway, Changa 388421, India
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Artificial Intelligence Group, Centre for Development of Advanced Computing, Jasola Vihar, New Delhi 110025, India
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Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Sena Pati Bapat Road, Pune 411004, India
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Department of Computer Science & Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), Charotar University of Science and Technology (CHARUSAT), Nadiad-Petlad Road, Highway, Changa 388421, India
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School of Computer Science and Engineering, The University of New South Wales, Sydney 1466, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Min Chen
Big Data Cogn. Comput. 2022, 6(1), 9; https://doi.org/10.3390/bdcc6010009
Received: 16 November 2021 / Revised: 26 December 2021 / Accepted: 7 January 2022 / Published: 11 January 2022
(This article belongs to the Special Issue Big Data and Internet of Things)
In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS). View Full-Text
Keywords: face detection; partial face detection; occluded face detection; deep learning; convolution neural network; FDDB dataset face detection; partial face detection; occluded face detection; deep learning; convolution neural network; FDDB dataset
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MDPI and ACS Style

Garg, D.; Jain, P.; Kotecha, K.; Goel, P.; Varadarajan, V. An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence. Big Data Cogn. Comput. 2022, 6, 9. https://doi.org/10.3390/bdcc6010009

AMA Style

Garg D, Jain P, Kotecha K, Goel P, Varadarajan V. An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence. Big Data and Cognitive Computing. 2022; 6(1):9. https://doi.org/10.3390/bdcc6010009

Chicago/Turabian Style

Garg, Dweepna, Priyanka Jain, Ketan Kotecha, Parth Goel, and Vijayakumar Varadarajan. 2022. "An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence" Big Data and Cognitive Computing 6, no. 1: 9. https://doi.org/10.3390/bdcc6010009

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