提高人脸检测效率:利用分类网络降低误报率

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2024-04-29 DOI:10.1016/j.array.2024.100347
Jianlin Zhang , Chen Hou , Xu Yang , Xuechao Yang , Wencheng Yang , Hui Cui
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引用次数: 0

摘要

卷积神经网络(CNN)的发展在人脸检测领域取得了显著进步,大大提高了准确率和召回率指标。精确度和召回率仍然是评估基于卷积神经网络的检测模型的关键;然而,人们普遍倾向于以牺牲误报率为代价来提高真阳性率。造成这种差异的一个关键问题是训练和评估数据集中缺乏伪人脸图像。这一缺陷损害了检测模型的回归能力,导致大量错误检测和定位不足。为了弥补这一不足,我们引入了 WIDERFACE 数据集,其中包含大量通过合并人类和动物面部特征创建的伪人脸图像。该数据集旨在加强训练阶段的误报检测。此外,我们还提出了一种新的人脸检测架构,在传统的人脸检测模型中加入了分类模型,以降低误报率并提高检测精度。我们在 WIDERFACE 和其他著名数据集上进行的对比分析表明,与现有的顶级人脸检测模型相比,我们的架构在保证真阳性率的同时,还能降低假阳性率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing face detection efficiency: Utilizing classification networks for lowering false positive incidences

The advancement of convolutional neural networks (CNNs) has markedly progressed in the field of face detection, significantly enhancing accuracy and recall metrics. Precision and recall remain pivotal for evaluating CNN-based detection models; however, there is a prevalent inclination to focus on improving true positive rates at the expense of addressing false positives. A critical issue contributing to this discrepancy is the lack of pseudo-face images within training and evaluation datasets. This deficiency impairs the regression capabilities of detection models, leading to numerous erroneous detections and inadequate localization. To address this gap, we introduce the WIDERFACE dataset, enriched with a considerable number of pseudo-face images created by amalgamating human and animal facial features. This dataset aims to bolster the detection of false positives during training phases. Furthermore, we propose a new face detection architecture that incorporates a classification model into the conventional face detection model to diminish the false positive rate and augment detection precision. Our comparative analysis on the WIDERFACE and other renowned datasets reveals that our architecture secures a lower false positive rate while preserving the true positive rate in comparison to existing top-tier face detection models.

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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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