电子学报2023，Vol.51Issue(11)：3282-3293,12.DOI:10.12263/DZXB.20220949
三维深度点云监督和置信度修正的人脸欺诈检测算法
3D Depth Point Cloud Supervision and Confidence Correction for Face Spoofing Detection
摘要
Abstract
Due to feasibility and friendly user interaction,deep learning-based face recognition and identity authenti-cation becomes one of the most popular artificial intelligence technologies in China.The face recognition and identity au-thentication system should secure that the captured face for verification is a living face rather than a fake face or called spoofing face.Otherwise,the output of the system is useless for business.Face spoofing detection or called living face de-tection mechanism is set in the front part of the system,and plays a key role in distinguishing a fake face from the input fac-es.Most current face anti-spoofing algorithms perform well in intra-dataset.However,the model training in lab is unable to simulate all aspects in the real-world application scenarios.As a result,the data distribution in source domain is not always similar to the data distribution in target domain,which causes the lab-trained algorithms barely perform as well as in lab.Al-though we can mitigate the performance degradation with the increase of detection feature types and dimensions,it tends to make the detection network very complex in structure and large in model size.In order to improve the generalization ability of model without resorting to large model,we design a face spoofing detection network using the 3D depth point cloud su-pervision and confidence correction scheme.The proposed approach consists of three major contributions.First,we design a shallow convolutional neural network called DenseBlockNet.It can well extract distinctive depth features between real faces and spoofing ones and has a small model size.Second,we establish the relationship between the 2D depth map pro-duced by DenseBlockNet and the coordinates of sampling points,and thus create a 3D depth point cloud.We adopt the Chamfer loss to minimize the distance between the learned 3D depth point cloud and the ground truth 3D depth point cloud label,and use the binary cross entropy loss to supervise the difference between the learned 2D depth map and the ground truth 2D depth map label.Third,we introduce a prediction confidence map to correct the error of the learned 3D depth point cloud,so that it can avoid overfitting in intra-datasets and obtain good generalization ability in inter-datasets.Extensive ex-periments are conducted on 5 popular presentation attack databases,namely Reply-attack,CASIA-FASD,MSU-MFSD,Rose-Youtu,and OULU-NPU.Compared with 8 representative methods including 2 SOTA methods,the proposed method can achieve the least or second least half-total-error-rates in either intra-dataset or inter-dataset tests.Besides,it has the smallest model,the least amount of model parameters and the lowest computational complexity.关键词
人脸欺诈检测/三维深度点云/3D深度点云监督/置信度修正/深度学习/泛化能力Key words
face spoofing detection/3D depth point cloud/3D point cloud supervision/confidence correction/deep learning/generalization ability分类
信息技术与安全科学引用本文复制引用
胡永健,蔡楚鑫,刘琲贝,王宇飞,廖广军..三维深度点云监督和置信度修正的人脸欺诈检测算法[J].电子学报,2023,51(11):3282-3293,12.基金项目
国家重点研发计划(No.2019QY2202) （No.2019QY2202）
广州黄埔开发区国际合作项目(No.2019GH16) （No.2019GH16）
2021年度广东省重点建设学科科研能力提升项目(No.2021ZDJS047) （No.2021ZDJS047）
教育部科技部司法鉴定技术应用与社会治理学科创新引智基地项目(No.B20077)National Key Research and Development Project(No.2019QY2202) （No.B20077）
Science and Technology Foundation of Guangzhou Huangpu Development District(No.2019GH16) （No.2019GH16）
2021 Scientific Research Capability Improve-ment Program for Key Discipline Construction of Guangdong Province(No.2021ZDJS047) （No.2021ZDJS047）
Forensic Sciences and Social Governance Disciplinary Innovation Base(No.B20077) （No.B20077）
