Page 489 - 《软件学报》2025年第9期

4400 软件学报 2025 年第 36 卷第 9 期

型的高泛化性能. 不过, 本文所提方法仍有改进的空间, 例如以更小的参数量来提升模型的鲁棒性等. 未来, 将基于
此进一步优化模型, 不断为人脸活体检测工作提供新的理论方案.

References:
[1] Zhang F, Zhao SK, Yuan C, Chen W, Liu XL, Zhao HJ. Research progress of face recognition anti-spoofing. Ruan Jian Xue Bao/Journal
of Software, 2022, 33(7): 2411–2446 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6590.htm [doi: 10.13328/j.cnki.
jos.006590]
[2] He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In: Proc. of the 2016 IEEE Conf. on Computer Vision
and Pattern Recognition. Las Vegas: IEEE, 2016. 770−778. [doi: 10.1109/CVPR.2016.90]
[3] Liu YJ, Jourabloo A, Liu XM. Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In: Proc. of the 2018 IEEE
Conf. on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 389−398. [doi: 10.1109/CVPR.2018.00048]
[4] Deb D, Jain AK. Look locally infer globally: A generalizable face anti-spoofing approach. IEEE Trans. on Information Forensics and
Security, 2020, 16: 1143–1157. [doi: 10.1109/TIFS.2020.3029879]
[5] Huang YH, Hsieh JW, Chang MC, Ke LP, Lyu S, Santra AS. Multi-teacher single-student visual Transformer with multi-level attention
for face spoofing detection. In: Proc. of the 32nd British Machine Vision Conf. BMVC, 2021. 22–25.
[6] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai XH, Unterthiner T, Dehghani M, Minderer M, Heigol G, Gelly S, Uszkoreit
J, Houlsby N. An image is worth 16x16 words: Transformers for image recognition at scale. In: Proc. of the 9th Int’l Conf. on Learning
Representations. OpenReview.net, 2021. 1–21.
[7] Shao R, Lan XY, Li JW, Yuen PC. Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In:
Proc. of the 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 10015−10023. [doi: 10.1109/
CVPR.2019.01026]
[8] Wang Z, Wang ZZ, Yu ZT, Deng WH, Li JH, Gao TT, Wang ZY. Domain generalization via shuffled style assembly for face anti-
spoofing. In: Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 4113–4123.
[doi: 10.1109/CVPR52688.2022.00409]
[9] Guo MH, Lu CZ, Liu ZN, Cheng MM, Hu SM. Visual attention network. Computational Visual Media, 2023, 9(4): 733–752. [doi: 10.
1007/s41095-023-0364-2]
[10] Zhang YF, Wang X, Liang J, Zhang Z, Wang L, Jin R, Tan TN. Free lunch for domain adversarial training: Environment label smoothing.
arXiv:2302.00194, 2023.
[11] Yang JW, Lei Z, Li SZ. Learn convolutional neural network for face anti-spoofing. arXiv:1408.5601, 2014.
[12] Li L, Feng XY, Boulkenafet Z, Xia ZQ, Li MM, Hadid A. An original face anti-spoofing approach using partial convolutional neural
network. In: Proc. of the 6th Int’l Conf. on Image Processing Theory, Tools and Applications. Oulu: IEEE, 2016. 1−6. [doi: 10.1109/
IPTA.2016.7821013]
[13] Feng LT, Po LM, Li YM, Xu XY, Yuan F, Cheung TCH, Cheung KW. Integration of image quality and motion cues for face anti-
spoofing: A neural network approach. Journal of Visual Communication and Image Representation, 2016, 38: 451–460. [doi: 10.1016/j.
jvcir.2016.03.019]
[14] Xu ZQ, Li S, Deng WH. Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In: Proc. of the 3rd IAPR
Asian Conf. on Pattern Recognition. Kuala Lumpur: IEEE, 2015. 141−145. [doi: 10.1109/ACPR.2015.7486482]
[15] Atoum Y, Liu YJ, Jourabloo A, Liu XM. Face anti-spoofing using patch and depth-based CNNs. In: Proc. of the 2017 IEEE Int’l Joint
Conf. on Biometrics (IJCB). Denver: IEEE, 2017. 319−328. [doi: 10.1109/BTAS.2017.8272713]
[16] Zhang KY, Yao TP, Zhang J, Tai Y, Ding SH, Li JL, Huang FY, Song HC, Ma LZ. Face anti-spoofing via disentangled representation
learning. In: Proc. of the 16th European Conf. on Computer Vision (ECCV 2020). Glasgow: Springer, 2020. 641−657. [doi: 10.1007/978-
3-030-58529-7_38]
[17] Yu ZT, Li XB, Niu XS, Shi JG, Zhao GY. Face anti-spoofing with human material perception. In: Proc. of the 16th European Conf. on
Computer Vision. Glasgow: Springer, 2020. 557−575. [doi: 10.1007/978-3-030-58571-6_33]
[18] Yu ZT, Zhao CX, Wang ZZ, Qin YX, Su Z, Li XB, Zhou F, Zhao GY. Searching central difference convolutional networks for face anti-
spoofing. In: Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 5294−5304. [doi: 10.
1109/CVPR42600.2020.00534]
[19] Wang GQ, Han H, Shan SG, Chen XL. Cross-domain face presentation attack detection via multi-domain disentangled representation
learning. In: Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 6677−6686. [doi: 10.