3D Depth Point Cloud Supervision and Confidence Correction for Face Spoofing Detection

HU Yong-jian; CAI Chu-xin; LIU Bei-bei; WANG Yu-fei; LIAO Guang-jun

doi:10.12263/DZXB.20220949

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3D Depth Point Cloud Supervision and Confidence Correction for Face Spoofing Detection

PAPERS | 更新时间：2025-12-08

- 3D Depth Point Cloud Supervision and Confidence Correction for Face Spoofing Detection
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3282-3293(2023)
- 作者机构：
  
  1.华南理工大学电子与信息学院，广东广州 510641
  2.广东警官学院刑事技术系，广东广州 510440
  3.中新国际联合研究院，广东广州 510700
- 作者简介：
- 基金信息：
  
  National Key Research and Development Project(2019QY2202);Science and Technology Foundation of Guangzhou Huangpu Development District(2019GH16);2021 Scientific Research Capability Improvement Program for Key Discipline Construction of Guangdong Province(2021ZDJS047);Forensic Sciences and Social Governance Disciplinary Innovation Base(B20077)
- DOI：10.12263/DZXB.20220949
  CLC： TP391.4;TP309.1
- Received：10 August 2022，
  
  Revised：2022-11-06，
  
  Published：25 November 2023
- 稿件说明：
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胡永健,蔡楚鑫,刘琲贝等.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.

HU Yong-jian,CAI Chu-xin,LIU Bei-bei,et al.3D Depth Point Cloud Supervision and Confidence Correction for Face Spoofing Detection[J].ACTA ELECTRONICA SINICA,2023,51(11):3282-3293.
胡永健,蔡楚鑫,刘琲贝等.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. DOI： 10.12263/DZXB.20220949.

HU Yong-jian,CAI Chu-xin,LIU Bei-bei,et al.3D Depth Point Cloud Supervision and Confidence Correction for Face Spoofing Detection[J].ACTA ELECTRONICA SINICA,2023,51(11):3282-3293. DOI： 10.12263/DZXB.20220949.

摘要

基于深度学习的人脸身份认证由于使用便捷和用户体验好，成为我国当今最受欢迎的人工智能技术应用之一.人脸识别和认证系统必须确保所比对的人脸是真实人脸，否则输出的结果没有任何商业价值.位于系统前端的人脸欺诈检测也称活体检测是保障人脸识别和认证系统有效输出的关键.现有人脸欺诈检测算法虽然库内性能尚佳，但由于实验室训练环境无法完全模拟真实应用场景，造成源域和目标域的数据在分布上存在差异，导致跨库检测性能明显下降.尽管通过增加检测特征的种类和个数可以改善算法性能，但会导致检测网络构造复杂，模型变大，计算复杂度增加.为了改善算法的跨库检测性能并降低计算的复杂度，本文提出一种基于三维（3D）深度点云监督和置信度修正机制的人脸欺诈检测算法.主要贡献包括：设计了DenseBlockNet，仅用较浅层的DenseBlockNet网络即可提取真假人脸之间具有很好区分度的深度信息特征，模型小；将DenseBlockNet输出的二维深度图与采样点位置进行关联，构造三维深度点云，采用倒角损失函数监督预测的深度点云与实际点云标签之间的三维空间距离，同时还采用图二元交叉熵损失监督预测的深度图与深度图标签之间的差异；在3D深度点云预测模块中引入置信度修正机制，修正二分类误差，同时避免库内过拟合，提高算法的泛化能力.所提出方法与包括2种最新文献的8种典型算法在Replay-attack、CASIA-FASD、MSU-MFSD、Rose-Youtu、OULU-NPU等5个主流人脸欺诈检测数据库上进行了充分的对比实验，实验结果表明，所提出的算法在库内和跨库检测中均能保持半总错误率最低或次低，且模型最小，参数量最少，计算复杂度最低.

Abstract

Due to feasibility and friendly user interaction

deep learning-based face recognition and identity authentication becomes one of the most popular artificial intelligence technologies in China. The face recognition and identity authentication 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 detection mechanism is set in the front part of the system

and plays a key role in distinguishing a fake face from the input faces. 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. Although 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 supervision 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 produced 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 experiments 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

Keywords

references

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