[1]陈凡[],童莹[],曹雪虹[]. 复杂环境下基于视觉显著性的人脸目标检测[J].计算机技术与发展,2017,27(01):48-52.
　CHEN Fan[],TONG Ying[],CAO Xue-hong[]. Face Target Detection of Visual Saliency in Complex Environment[J].,2017,27(01):48-52.
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 复杂环境下基于视觉显著性的人脸目标检测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年01期
页码:
48-52
栏目:
智能、算法、系统工程
出版日期:
2017-01-10

文章信息/Info

Title:
 Face Target Detection of Visual Saliency in Complex Environment
文章编号:
1673-629X（2017)01-0048-05
作者:
 陈凡[1]; 童莹[2]; 曹雪虹[2]
 1.南京邮电大学 通信与信息工程学院；2.南京工程学院 通信工程学院
Author(s):
 CHEN Fan[1]; TONG Ying[2]; CAO Xue-hong[2]
关键词:
 ; 复杂环境; 基于图论的视觉显著性算法; 视觉显著性; 人脸检测
Keywords:
 ; complex environment; GBVS; visual saliency; face detection
分类号:
TP273
文献标志码:
A
摘要:
 当前复杂环境下人脸识别因受目标背景杂乱等因素影响,分类效果不理想。针对此问题,提出了基于视觉显著性的人脸目标检测方法,利用基于图论的视觉显著性算法( Graph-Based Visual Saliency,GBVS)提取复杂环境中的人脸目标的显著图,对显著图进行阈值分割和形态学操作得到二值人脸目标区域,以区域质心为中心,以区域边缘到质心的最小距离为边长,截取图像中的准确人脸区域,实现复杂环境下的人脸目标检测。在LFW数据库上的实验结果表明,所提算法能够准确地完成“抠图”的任务,具有较为理想的人脸检测效果,因算法实现过程无需人工干预,可有效摒除杂乱背景干扰,且提高了检测速度,实现了无监督的人脸检测,为智能化人脸识别提供了理论研究基础。
Abstract:
 At present,the classification accuracy of face recognition in complex environment is not satisfied because of the background clutter and other factors. To solve this problem,a face detection algorithm based on visual saliency has been proposed,in which graph based visual salient algorithm is employed to extract salient maps of face region in complex environment and then threshold segmentation and morphological operations is run to get the binary face marks and the centroid is taken as center as well as the minimum distance of the region edge and centroid as side length to crop the accurate area of target face to achieve the goal of face detection in complex environ-ment. Results of experiments on the LFW image database show that the proposed algorithm can accurately fulfill“matting” tasks and a-chieve good results in face detection,the process of which needs no artificial participation,and can effectively exclude the interference of pell-mell background besides having improved the detection rate,realizing the unsupervised face detection,providing a theoretical foun-dation for intelligent research of face recognition.

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更新日期/Last Update: 2017-04-01