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Cited 10 time in webofscience Cited 12 time in scopus
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Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approachopen access

Authors
Mamieva, DilnozaAbdusalomov, Akmalbek BobomirzaevichMukhiddinov, MukhriddinWhangbo, Taeg Keun
Issue Date
Jan-2023
Publisher
MDPI
Keywords
face detection; retina net; region offering network; deep learning
Citation
SENSORS, v.23, no.1
Journal Title
SENSORS
Volume
23
Number
1
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86967
DOI
10.3390/s23010502
ISSN
1424-8220
Abstract
Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning's quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection software, the difficulty of detecting small, scale, position, occlusion, blurring, and partially occluded faces in uncontrolled conditions is one of the problems of face identification that has been explored for many years but has not yet been entirely resolved. In this paper, we propose Retina net baseline, a single-stage face detector, to handle the challenging face detection problem. We made network improvements that boosted detection speed and accuracy. In Experiments, we used two popular datasets, such as WIDER FACE and FDDB. Specifically, on the WIDER FACE benchmark, our proposed method achieves AP of 41.0 at speed of 11.8 FPS with a single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are results among one-stage detectors. Then, we trained our model during the implementation using the PyTorch framework, which provided an accuracy of 95.6% for the faces, which are successfully detected. Visible experimental results show that our proposed model outperforms seamless detection and recognition results achieved using performance evaluation matrices.
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Whangbo, Taeg Keun
College of IT Convergence (컴퓨터공학부(컴퓨터공학전공))
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