Face detection using deep learning: An improved faster RCNN approach

Face detection using deep learning: An improved faster RCNN approach

In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, inclu...

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Bibliographic Details
Main Authors: SUN, Xudong, WU, Pengcheng, HOI, Steven C. H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
Face detection
Faster RCNN
Convolutional neural networks (CNN)
Feature concatenation
Hard negative miningMulti-scale training
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/3998
https://ink.library.smu.edu.sg/context/sis_research/article/5000/viewcontent/Face_detection_using_deep_learning__An_improved_faster_RCNN_approach.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark

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