|Table of Contents|

Multi-view Face Detection Based on the Pixel Differential Covariance Feature(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年02期
Page:
73-79
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Multi-view Face Detection Based on the Pixel Differential Covariance Feature
Author(s):
Lü JingXue YafeiGu Jingping
(Zhongbei College,Nanjing Normal University,Danyang 212300,China)
Keywords:
face detectionpixel differential featurepixel differential covariance featureface proposal
PACS:
TP391.41
DOI:
10.3969/j.issn.1672-1292.2022.02.011
Abstract:
PDV is a discriminative first order operator,how to encode high order statistics into PDV is still unaddressed. In this paper,we investigate the covariance of the PDV in a local region,and propose a novel pixel differential covariance feature(PDCF),significantly improving the performance for multi-view face detection. Following the PDV based object detection pipeline,multiple channel maps are initially prepared before calculating pixel differential feature. Afterwards,covariance matrix of the PDV is computed to obtain the pair-wise correlation between arbitrary two elements. Finally,a face proposal mechanism is designed to generate face candidates and a PDCF based detector is further refined,achieving real-time running speed. Experimental results show that,the proposed method outperforms other state-of-the-art methods and can be deployed on the edge-computing device.

References:

[1] 曾建凡. 多角度人脸检测与识别方法研究[J]. 电子设计工程,2017,25(11):41-44.
[2]YANG B,YAN J J,LEI Z,et al. Aggregate channel features for multi-view face detection[C]//Proceedings of the 2014 International Joint Conference on Biometrics. Clearwater,USA:IEEE,2014.
[3]LI J G,ZHANG Y M. Learning SURF cascade for fast and accurate object detection[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland,USA:IEEE,2013.
[4]SHEN J F,ZUO X,LI J,et al. A novel pixel neighborhood differential statistic feature for pedestrian and face detection[J]. Pattern Recognition,2017,63:127-138.
[5]张明浩,杨耀权,靳渤文. 基于图像增强技术的SURF特征匹配算法研究[J]. 自动化与仪表,2019,34(9):98-102.
[6]UEHARA K,SAKANASHI H,NOSATO H,et al. Object detection of satellite images using multi-channel higher-order local autocorrelation[C]//2017 IEEE International Conference on Systems,Man,and Cybernetics. Banff,Canada:IEEE,2017.
[7]MATHIAS M,PEDERSOLI R B M,VAN R B M. Gool,face detection without bells and whistles[C]//Proceedings of the 13th European Conference on Computer Vision. Zurich,Switzerland:Springer,2014.
[8]耿渊哲. 基于LBP采样学习的人脸识别研究[J]. 计算机与数字工程,2018,46(2):371-374.
[9]YANG S,LUO P,LOY C C,et al. Faceness-Net:face detection through deep facial part responses[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(8):1845-1859.
[10]CHEN D,HUA G,WEN F,et al. Supervised transformer network for efficient face detection[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdan,Netherlands:Springer,2016.
[11]LI J,WANG Y B,WANG C A,et al. DSFD:dual shot face detector[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach,USA:IEEE,2019.
[12]ZHANG S,CHI C,LEI Z,et al. RefineFace:refinement neural network for high performance face detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,43(11):4008-4020.

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