[1]王成济,罗志明,钟准,等.一种多层特征融合的人脸检测方法[J].智能系统学报,2018,13(1):138-146.[doi:10.11992/tis.201707018]
　WANG Chengji,LUO Zhiming,ZHONG Zhun,et al.Face detection method fusing multi-layer features[J].CAAI Transactions on Intelligent Systems,2018,13(1):138-146.[doi:10.11992/tis.201707018]
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一种多层特征融合的人脸检测方法

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 13 期数: 2018年第1期 页码: 138-146 栏目: 学术论文—机器感知与模式识别 出版日期: 2018-01-24
Title:
Face detection method fusing multi-layer features
作者:
王成济1,2, 罗志明1,2, 钟准1,2, 李绍滋1,2
1. 厦门大学 智能科学与技术系, 福建 厦门 361005;
2. 厦门大学 福建省类脑计算技术及应用重点实验室, 福建 厦门 361005
Author(s):
WANG Chengji1,2, LUO Zhiming1,2, ZHONG Zhun1,2, LI Shaozi1,2
1. Intelligent Science & Technology Department, Xiamen University, Xiamen 361005, China;
2. Fujian Key Laboratory of Brain-inspired Computing Technique and Applications, Xiamen University, Xiamen 361005, China
关键词:
人脸检测; 多姿态; 多尺度; 遮挡; 复杂场景; 卷积神经网络; 特征融合; 非极大值抑制
Keywords:
face detection; multi pose; multi scale; occlude; complex scenes; convolutional neural network; feature fusion; non-maximum suppression
分类号:
TP391.41
DOI:
10.11992/tis.201707018
摘要:
由于姿态、光照、尺度等原因，卷积神经网络需要学习出具有强判别力的特征才能应对复杂场景下的人脸检测问题。受卷积神经网络中特定特征层感受野大小限制，单独一层的特征无法应对多姿态多尺度的人脸，为此提出了串联不同大小感受野的多层特征融合方法用于检测多元化的人脸；同时，通过引入加权降低得分的方法，改进了目前常用的非极大值抑制算法，用于处理由于遮挡造成的相邻人脸的漏检问题。在FDDB和WiderFace两个数据集上的实验结果显示，文中提出的多层特征融合方法能显著提升检测结果，改进后的非极大值抑制算法能够提升相邻人脸之间的检测准确率。
Abstract:
To address the issues of pose, lighting variation, and scales, convolutional neural networks (CNNs) need to learn features with strong discrimination handle the face detection problem in complex scenes. Owing to the size limitations of the specific feature layer’s receptive field in convolutional neural networks, the features computed from a single layer of the CNNs are incapable of dealing with faces in multi poses and multi scales. Therefore, a multi-layer feature fusion method that is realized by fusing the different sizes of receptive fields is proposed to detect diversified faces. Moreover, via introducing the method of weighted score decrease, the present usual non-maximum suppression algorithm was improved to deal with the detection omission of neighboring faces caused by shielding. The experiment results with the FDDB and WiderFace datasets demonstrated that the fusion method proposed in this study can significantly boost detection performance, while the improved non-maximum suppression algorithm can increase the detection accuracy between neighboring faces.
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备注/Memo

收稿日期:2017-07-10。
基金项目:国家自然科学基金项目（61572409, 61402386, 81230087, 61571188）.
作者简介:王成济,男,1993年生,硕士研究生,主要研究方向为视频目标检测和图像分割;罗志明,男,1989年生,博士研究生,主要研究方向为图像分割、目标检测、医学图像分析。发表学术论文8篇;李绍滋,男,1963年生,教授,博士生导师,主要研究方向为计算机视觉、机器学习和数据挖掘。先后主持或参加过多项国家863项目、国家自然科学基金项目、教育部博士点基金项目、省科技重点项目等多个项目的研究,发表学术论文300多篇。
通讯作者:李绍滋.E-mail:szlig@xmu.edu.cn.

更新日期/Last Update: 2018-02-01
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