Abstract
Face anti-spoofing plays an important role in face recognition system to prevent security vulnerability. It acts as an important step to select the face image to the face recognition system. Previous works have provided many databases for face anti-spoofing, but they either contain too few subjects or contain a single modal. Moreover, due to the limited information of RGB images, the effect of face anti-spoofing is difficult to be further improved, High Dynamic Range (HDR) can be a good choice while many devices now support capturing HDR images. To facilitate further studies on face anti-spoofing, we in this work build a dataset with 50 subjects for face anti-spoofing containing 2100 videos, which containing two different modalities, HDR and Near Infrared Ray (NIR). We further study the influences of different modalities on the performance of face anti-spoofing by extensively experimenting with different combinations of modalities with an end-to-end deep learning model, and find that the HDR information contributes most among different modalities. Finally, we verify these findings on the CASIA-FASD dataset which demonstrates better performance of the proposed model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE (2011)
Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNs. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 319–328. IEEE (2017)
Bhattacharjee, S., Mohammadi, A., Marcel, S.: Spoofing deep face recognition with custom silicone masks. Tech. rep. (2018)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2636–2640. IEEE (2015)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Process. Lett. 24(2), 141–145 (2017)
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: Proceedings of the 11th International Conference of the Biometrics Special Interest Group. No. EPFL-CONF-192369 (2012)
Chingovska, I., Erdogmus, N., Anjos, A., Marcel, S.: Face recognition systems under spoofing attacks. In: Bourlai, T. (ed.) Face Recognition Across the Imaging Spectrum, pp. 165–194. Springer, Cham (2016)
Costa-Pazo, A., Bhattacharjee, S., Vazquez-Fernandez, E., Marcel, S.: The replay-mobile face presentation-attack database. In: Biometrics Special Interest Group (2016)
Di, W., Hu, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)
Eilertsen, G., Kronander, J., Denes, G., Mantiuk, R., Unger, J.: HDR image reconstruction from a single exposure using deep CNNs. ACM Trans. Graphics (TOG) 36(6), 178 (2017)
de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: LBP–TOP based countermeasure against face spoofing attacks. In: Park, J.-I., Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37410-4_11
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: alexnet-level accuracy with 50x fewer parameters and \(<\)0.5mb model size (2016)
Jackson, A.S., Bulat, A., Argyriou, V., Tzimiropoulos, G.: Large pose 3D face reconstruction from a single image via direct volumetric CNN regression. In: International Conference on Computer Vision (2017)
Kemelmacher-Shlizerman, I., Seitz, S., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: Computer Vision & Pattern Recognition (2016)
Komulainen, J., Boulkenafet, Z., Hadid, A.: Face anti-spoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Process. Lett. PP(99), 1 (2017)
Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: 2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 1–6. IEEE (2016)
Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 389–398 (2018)
Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE (2011)
Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera (2007)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)
Sun, X., Huang, L., Liu, C.: Context based face spoofing detection using active near-infrared images. In: International Conference on Pattern Recognition, pp. 4262–4267 (2017)
Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 504–517. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15567-3_37
Tang, Y., Wang, X., Jia, X., Shen, L.: Fusing multiple deep features for face anti-spoofing. In: Zhou, J., et al. (eds.) CCBR 2018. LNCS, vol. 10996, pp. 321–330. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97909-0_35
Tang, Y., Wang, X., Jia, X., Shen, L.: Fusing multiple deep features for face anti-spoofing. In: Chinese Conference on Biometric Recognition, pp. 321–330 (2018)
Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. arXiv preprint arXiv:1408.5601 (2014)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks, pp. 26–31 (2012)
Acknowledgments
This work is supported by the Fundamental Research Funds for the Central Universities (2017JBZ108).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Geng, B., Lang, C., Xing, J., Feng, S., Jun, W. (2019). MFAD: A Multi-modality Face Anti-spoofing Dataset. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-29911-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29910-1
Online ISBN: 978-3-030-29911-8
eBook Packages: Computer ScienceComputer Science (R0)