Pix2pix network for fingerprint texture image synthesis
Resumo
GANs (Generative Adversarial Networks) were proposed to generate realistic synthetic images. In this work, we will discuss the use of GANs as alternative reconstruction of different fingerprint images from the original ones. The samples result in the same person fingerprint but obtained with other textures. Thus, it is intended to contribute to improving the method to increase databases with new samples, incorporating textures, when the quantities are insufficient for any purpose. To verify the similarity of the synthesized images with the original ones, a convolutional Xception network and the RMSE metric are used. The results obtained with fingerprint images of 3 persons, 20 of each finger, and 4 different textures, showed the tradeoff between similarity, recognizability, and the number of epochs of the Pix2pix training.
Palavras-chave:
GANs, adversarial neural network, generator and discriminator, fingerprints, textures
Referências
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A. Rattani, Z. Akhtar, and G. Foresti, “A preliminary study on identifying fabrication material from fake fingerprint images,” IEEE Symposium Series on Computational Intelligence, Cape Town, África do Sul, 7 a 10 de dezembro, pp.362-366, 2015.
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C. Li and M. Wand, “Precomputed real-time texture synthesis with Markovian generative adversarial networks,” Computer Vision (ECCV), 14th European Conference, vol. 9907, Amsterdã, Holanda, 8 a 16 de outubro, pp.702-716, 2016.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” ArXiv:1512.03385 [Cs], 2015.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 23-28 junho, 2014.
K. Zuiderveld, “Contrast limited adaptive histogram equalization,” In Graphics Gems: Vol. VIII.5 (Paul S. Hackbert eds.), Amsterdã, Elsevier, (pp. 474-485), 1994.
S. M. Pizer, E. P. Amburn, J.D Austin, R. Cromartie, A. Geselowitz, T. Greer, and K. Zuiderveld, “Adaptive histogram equalization and Its variations,” Computer Vision, Graphics, and Image Processing, vol.39, n.3, pp.355-368, 1987.
R. C. Gonzalez and R. C. Woods, Processamento Digital de Imagens, (3rd ed.). Pearson Prentice Hall; pp. 46, 2010.
O. M. Filho and H. V. Neto, Processamento Digital de Imagens, Brasport; pp. 27, 1999.
L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo, “Face x-ray for more general face forgery detection,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 14 a 19 de junho, pp.5000-5009, 2020.
J. T. Jayan and R. P. Aneesh, “Image quality measures based face spoofing detection algorithm for online social media,” International CET Conference on Control, Communication, and Computing (IC4), Thiruvanthapuram, India, 5 a 7 de julho, pp.245-249, 2018.
A. Rattani, Z. Akhtar, and G. Foresti, “A preliminary study on identifying fabrication material from fake fingerprint images,” IEEE Symposium Series on Computational Intelligence, Cape Town, África do Sul, 7 a 10 de dezembro, pp.362-366, 2015.
N. A. Kulkarni, and L. J. Sankpal, “Efficient approach determination for fake biometric Detection,” International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, 17 a 18 de agosto, pp.1-4, 2017.
A. Singh, G. Jaswal, and A. Nigam, “FDSNet: Finger dorsal image spoof detection network using light field camera,” IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA), Kobe, Japão, 13 a 14 de julho, pp.1-9, 2019.
C. Zaghetto, M. Mendelson, A. Zaghetto, and F. de B. Vidal, “Liveness detection on touchless fingerprint devices using texture descriptors and artificial neural networks,” IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 1 a 4 de outubro, pp.406-412, 2017.
A. Rattani, and A. Ross, “Minimizing the impact of spoof fabrication material on fingerprint liveness detector,” IEEE International Conference on Image Processing (ICIP), Paris, França, 27 a 30 de outubro, pp.4992-4996, 2014.
Khutlang, R., and Nelwamondo, F. V. (2014) Novelty detection-based internal fingerprint segmentation in Optical Coherence Tomography Images. Second International Symposium on Computing and Networking, Shizuoka, Japão, 10 a 12 de dezembro, pp.556-559.
F. Pala and B. Bhanu, “On the accuracy and robustness of deep triplet embedding for fingerprint liveness detection,” IEEE International Conference on Image Processing (ICIP), Beijing, China, 17 a 20 de setembro, pp.116-120, 2017.
T. Chugh, and A. K. Jain, “Fingerprint spoof detector generalization,” IEEE Transactions on Information Forensics and Security, vol.16, p.42-55, 2020.
G. Wang, W. Kang, Q. Wu, Z. Wang, and J. Gao, “Generative adversarial network (GAN) based data augmentation for palmprint recognition,” Digital Image Computing: Techniques and Applications (DICTA), Canberra, Australia, 10 a 13 de dezembro, pp.1-7, 2018.
S. Tariq, S. Lee, H. Kim, Y. Shin, and S. S. Woo, “Detecting both machine and human created fake face images In the wild,” 2nd International Workshop on Multimedia Privacy and Security - MPS '18, Toronto, Canadá, outubro, pp.81-87, 2018.
T. Tan, X. Wang, Y. Fang, and W. Zhang, “The impact of data correlation on identification of computer-generated face images,” 14th Chinese Conference on Biometric Recognition, CCBR 2019, vol.11818, Zhuzhou, China, 12 a 13 de outubro, p.155-162, 2019.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” ArXiv:1406.2661 [Cs, Stat], 2014.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image translation with conditional adversarial networks,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 21 a 26 de julho, pp.5967-5976, 2017.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 de julho, pp.1800-1807, 2017.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” Medical Image Computing and Computer-Assisted Intervention - MICCAI, vol.9351, Munique, Alemanha, 5 a 9 de outubro. pp.234-241, 2015.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” 4th International Conference on Learning Representations, (ICLR), San Juan, Porto Rico, 2 a 4 de maio, 2016.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol.60, n.6, pp.84-90, 2012.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning, vol.37, Lille, França, 6 a 11 de julho, pp.448-456, 2015.
C. Li and M. Wand, “Precomputed real-time texture synthesis with Markovian generative adversarial networks,” Computer Vision (ECCV), 14th European Conference, vol. 9907, Amsterdã, Holanda, 8 a 16 de outubro, pp.702-716, 2016.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” ArXiv:1512.03385 [Cs], 2015.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 23-28 junho, 2014.
K. Zuiderveld, “Contrast limited adaptive histogram equalization,” In Graphics Gems: Vol. VIII.5 (Paul S. Hackbert eds.), Amsterdã, Elsevier, (pp. 474-485), 1994.
S. M. Pizer, E. P. Amburn, J.D Austin, R. Cromartie, A. Geselowitz, T. Greer, and K. Zuiderveld, “Adaptive histogram equalization and Its variations,” Computer Vision, Graphics, and Image Processing, vol.39, n.3, pp.355-368, 1987.
R. C. Gonzalez and R. C. Woods, Processamento Digital de Imagens, (3rd ed.). Pearson Prentice Hall; pp. 46, 2010.
O. M. Filho and H. V. Neto, Processamento Digital de Imagens, Brasport; pp. 27, 1999.
Publicado
22/11/2021
Como Citar
CORDEIRO, Jader dos Santos Teles; SAITO, José Hiroki.
Pix2pix network for fingerprint texture image synthesis. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 13-18.
DOI: https://doi.org/10.5753/wvc.2021.18882.
