引入跨批存储机制度量学习的人脸活体检测

doi:10.3778/j.issn.1002-8331.2206-0270

摘要/Abstract

摘要： 针对人脸活体检测中存在的嵌入特征混叠、泛化能力差的问题，采用异常检测的方法来学习活体样本的一个紧凑表示空间，并通过像素级的辅助监督来获得更细粒度的活性特征；为了获得更清晰的分类边界，引入多尺度三元组损失来优化模型，采用批内和批间样本挖掘相结合的方法，来扩大样本挖掘范围，以获得更多有效的样本对。通过在公开数据集OULU、Replay Attack和CASIA上的对比实验，证明了算法的鲁棒性和泛化能力。

关键词: 人脸活体检测, 异常检测, 三元组损失, 样本挖掘策略

Abstract: Aiming at the problems of embedding feature aliasing and poor generalization ability in face liveness detection,it uses the method of anomaly detection to learn a compact representation space of living samples and get finer liveness features through pixel-level assisted supervision. To get a clearer classification boundary, multi-scale tuple loss is introduced to optimize the model, and the method of combining in-batch and inter-batch sample mining is used to expand the scope of sample mining to obtain more effective sample pairs. Finally, the robustness and generalization of the algorithm are demonstrated by comparing experiments on open datasets OULU, Replay Attack, and CASIA.

Key words: face liveness detection, anomaly detection, triplet loss, sample mining strategy

蔡体健, 刘文鑫, 尘福春, 陈均, 罗词勇. 引入跨批存储机制度量学习的人脸活体检测[J]. 计算机工程与应用, 2023, 59(23): 183-190.

CAI Tijian, LIU Wenxin, CHEN Fuchun, CHEN Jun, LUO Ciyong. Face Liveness Detection Algorithm Based on Metric Learning-Cross Batch Storage Mechanism[J]. Computer Engineering and Applications, 2023, 59(23): 183-190.

参考文献

[1] BOULKENAFET Z，KOMULAINEN J，HADID A.Face spoofing detection using colour texture analysis[J].IEEE Transactions on Information Forensics and Security，2017，11（8）：1818-1830.
[2] LI X B，KOMULAINEN J，ZHAO G Y，et al.Generalized face anti-spoofing by detecting pulse from face videos[C]//Proceedings of IEEE 23rd International Conference on Pattern Recognition.Washington，USA：IEEE，2016：4239-4244.
[3] LIU S Q，LAN X Y，YUEN P C.Multi-channel remote photoplethysmography correspondence feature for 3D mask face presentation attack detection[J].IEEE Transactions on Information Forensics and Security，2021，16：2683-2696.
[4] YANG J，LEI Z，LI S Z.Learn convolutional neural network for face anti-spoofing[J].arXiv：1408.5601，2014.
[5] CHEN H，HU G，LEI Z，et al.Attention-based two-stream convolutional networks for face spoofing detection[J].IEEE Transactions on Information Forensics and Security，2019，15：578-593.
[6] 王宏飞，程鑫，赵祥模，等.光流与纹理特征融合的人脸活体检测算法[J].计算机工程与应用，2022，58（6）：170-176.
WANG H F，CHENG X，ZHAO X M，et al.Face Liveness detection based on fusional optical flow and texture features[J].Computer Engineering and Applications，2022，58（6）：170-176.
[7] YU Z，QIN Y，ZHAO H，et al.Dual-cross central difference network for face anti-spoofing[J].arXiv：2105.01290，2021.
[8] SCHROFF F，KALENICHENKO D，PHILBIN J.Facenet：a unified embedding for face recognition and clustering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington，USA：IEEE，2015：815-823.
[9] PéREZ-CABO D，JIMéNEZ-CABELLO D，COSTA-PAZO A，et al.Deep anomaly detection for generalized face anti-spoofing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.Washington，USA：IEEE，2019.
[10] FENG H，HONG Z，YUE H，et al.Learning generalized spoof cues for face anti-spoofing[J].arXiv：2005.03922，2020.
[11] LI Z，LI H，LAM K Y，et al.Unseen face presentation attack detection with hypersphere loss[C]//2020 IEEE International Conference on Acoustics，Speech and Signal Processing（ICASSP），2020.
[12] HERMANS A，BEYER L，LEIBE B.In defense of the triplet loss for person re-identification[J].arXiv：1703. 07737，2017.
[13] DENG J，DONG W，SOCHER R，et al.Imagenet：a large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.Washington，USA：IEEE，2009：248-255.
[14] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington，USA：IEEE，2016：770-778.
[15] WANG X，ZHANG H，HUANG W，et al.Cross-batch memory for embedding learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington，USA：IEEE，2020：6388-6397.
[16] HE K，FAN H，WU Y，et al.Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington，USA：IEEE，2020：9729-9738.
[17] SUH Y，HAN B，KIM W，et al.Stochastic class-based hard example mining for deep metric learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington，USA：IEEE，2019：7251-7259.
[18] QIAN Q，SHANG L，SUN B，et al.SoftTriple loss：deep metric learning without triplet sampling[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.Washington，USA：IEEE，2019：6450-6458.
[19] BOULKENAFET Z，KOMULAINEN J，LI L，et al.OULU-NPU：a mobile face presentation attack database with real-world variations[C]//2017 12th IEEE International Conference on Automatic Face & Gesture Recognition（FG 2017）.Washington，USA：IEEE，2017：612-618.
[20] ZHANG Z，YAN J，LIU S，et al.A face anti-spoofing database with diverse attacks[C]//2012 5th IAPR International Conference on Biometrics（ICB）.Washington，USA：IEEE，2012：26-31.
[21] CHINGOVSKA I，ANJOS A，MARCEL S.On the effectiveness of local binary patterns in face anti-spoofing[C]//Proceedings of the International Conference of Biometrics Special Interest Group（BIOSIG）.Washington，USA：IEEE，2012：1-7.
[22] KINGMA D P，BA J.Adam：a method for stochastic optimization[J].arXiv.org：1412.6980，2014.
[23] YANG X，LUO W，BAO L，et al.Face anti-spoofing：model matters，so does data[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington，USA：IEEE，2019：3507-3516.
[24] CHEN B，YANG W，LI H，et al.Camera invariant feature learning for generalized face anti-spoofing[J].IEEE Transactions on Information Forensics and Security，2021，16：2477-2492.
[25] YU Z，LI X，NIU X，et al.Face anti-spoofing with human material perception[C]//European Conference on Computer Vision.Cham：Springer，2020：557-575.
[26] YU Z，LI X，SHI J，et al.Revisiting pixel-wise supervision for face anti-spoofing[J].IEEE Transactions on Biometrics，Behavior，and Identity Science，2021，3（3）：285-295.
[27] LIU Y，STEHOUWER J，LIU X.On disentangling spoof trace for generic face anti-spoofing[C]//European Conference on Computer Vision.Cham：Springer，2020：406-422.
[28] ALMEIDA W R，ANDALóF A，PADILHA R，et al.Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor- aware loss function[J].PLoS One，2020，15（9）：e0238058.
[29] BOULKENAFET Z，KOMULAINEN J，HADID A.Face anti-spoofing using speeded-up robust features and fisher vector encoding[J].IEEE Signal Processing Letters，2016，24（2）：141-145.
[30] ZHENG W，YUE M，ZHAO S，et al.Attention-based spatial-temporal multi-scale network for face anti-spoofing[J].IEEE Transactions on Biometrics，Behavior，and Identity Science，2021，3（3）：296-307.
[31] ZHANG K Y，YAO T，ZHANG J，et al.Face anti-spoofing via disentangled representation learning[C]//European Conference on Computer Vision.Cham：Springer，2020：641-657.
[32] VAN DER MAATEN L，HINTON G.Visualizing data using t-SNE[J].Journal of Machine Learning Research，2008，9（11）：2579-2605.