细粒度深度特征掩码估计的遮挡人脸识别算法

王富平; 王定莎; 李藕; 刘卫华; 刘鸿玮

doi:10.7652/xjtuxb202502017

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细粒度深度特征掩码估计的遮挡人脸识别算法

更新时间：2026-01-06

- 细粒度深度特征掩码估计的遮挡人脸识别算法
- Occluded Face Recognition Algorithm Based on Fine-Grained Deep Feature Mask Estimation
- 西安交通大学学报 2025年59卷第2期页码：170-179
- 作者机构：
  
  西安邮电大学通信与信息工程学院， 710121，西安
- 作者简介：
  
  王富平（1987—），男，博士，讲师。
- 基金信息：
  
  陕西省科技厅重点研发计划资助项目(2024SF-YBXM-663)
- DOI：10.7652/xjtuxb202502017
  中图分类号： TP391
- 收稿：2024-08-11，
  
  网络首发：2024-10-30，
  
  纸质出版：2025-02-10
- 稿件说明：
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王富平, 王定莎, 李藕, 等. 细粒度深度特征掩码估计的遮挡人脸识别算法[J]. 西安交通大学学报, 2025,59(2):170-179.

WANG Fuping, WANG Dingsha, LI Ou, et al. Occluded Face Recognition Algorithm Based on Fine-Grained Deep Feature Mask Estimation[J]. Journal of Xi’an Jiaotong University, 2025, 59(2): 170-179.
王富平, 王定莎, 李藕, 等. 细粒度深度特征掩码估计的遮挡人脸识别算法[J]. 西安交通大学学报, 2025,59(2):170-179. DOI： 10.7652/xjtuxb202502017.

WANG Fuping, WANG Dingsha, LI Ou, et al. Occluded Face Recognition Algorithm Based on Fine-Grained Deep Feature Mask Estimation[J]. Journal of Xi’an Jiaotong University, 2025, 59(2): 170-179. DOI： 10.7652/xjtuxb202502017.

摘要

针对人脸遮挡产生面部结构信息丢失，从而导致人脸识别准确率降低的问题，提出了一种细粒度深度特征掩码估计的遮挡人脸识别算法。首先，将人脸图像输入特征金字塔网络中，从而得到多尺度深度语义特征；其次，将从特征金字塔网络提取的特征经过空洞卷积处理后，与MobileNetV3网络提取的精细浅层特征进行融合，并以像素级二值掩码为标签训练网络以获得细粒度特征掩码；进而，利用该深度特征掩码与深层特征相乘，以抑制由遮挡产生的干扰特征，获得更准确的人脸表征；最后，采用余弦损失和掩码估计损失联合训练网络，提高遮挡人脸识别算法的性能。在LFW数据集基础上创建了口罩、围巾和中心遮挡3种类型的人脸遮挡数据集，实验结果表明：在不同的数据集上，所提算法与现有算法相比均具有更高的识别准确率，并在不同类型遮挡情况下均能获得十分稳定的人脸识别结果；所提算法在数据集LFW和LFW口罩遮挡上的识别准确率分别达到了99.38%和98.42%，在数据集LFW围巾遮挡和LFW中心遮挡上的识别准确率分别达到了98.72%和98.65%，均优于对比算法。

Abstract

To solve the problem of accuracy decrease in facial recognition caused by the loss of facial structural information in the case of facial occlusion

an occluded face recognition algorithm based on fine-grained deep feature mask estimation was proposed. Firstly

the face image was fed into feature pyramid network (FPN) to obtain multi-scale deep semantic features. Next

the features extracted from the FPN were processed by the atrous convolution and fused with shallow features extracted using MobileNetV3; a pixel-wise binary mask was used as label to train the network to obtain the fine-grained deep feature mask; this deep feature mask was multiplied with the deep features to suppress the corrupted feature produced by occlusion and to obtain the better face representation. Finally

CosFace loss and mask estimation loss were jointly used to train the network to improve the performance of occluded face recognition algorithm. Three face occlusion datasets with mask

scarf

and center occlusion were created based on the LFW dataset

respectively. The experimental results show that on four different datasets

the proposed algorithm was more accurate than existing algorithms and obtained very stable face recognition results under different occlusion situations. The recognition accuracy on LFW and LFW-mask-occlusion datasets reached 99.38% and 98.42%

respectively and that on LFW-scarf-occlusion and LFW-center-occlusion datasets reached 98.72% and 98.65%

respectively

outperforming the algorithms compared.

关键词

Keywords

references

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