2D-FACE ALIGNMENT WITH CYCLEGAN FACE AGING IMAGE-TO-IMAGE TRANSLATION
DOI:
https://doi.org/10.11113/aej.v12.17492Keywords:
Image-to-image translation, Face Alignment, Deep Learning, Face Alignment, image-to-image translation, CycleGAN, Deep Learning, AIAbstract
Face alignment is one of the pre-processing processes where the face plays a crucial part in image tasks and computer vision. As part of the pre-processing step, it is the first step taken before implementing an image processing task. By aligning face, it is expected to improve the network model performance, because good input data is now represented in the network model. This research aims to see whether pre-processing the input data can improve the network model performance. A 2D-face alignment technique is used to align all the input images. All the input image that is already being aligned is used as the input image for the CycleGAN face aging image-to-image translation model. In this work, the CycleGAN network model is used to translate an image of a young face to their older version and vice versa. The result obtained shows that if the network model is presented with a properly aligned face, it can translate the image into a younger or older version better than when presented with a non-aligned face.
References
P. Isola, Y. Zhu, T.Zhou, A. A. Efros. 2016. Image-to-Image Translation with Conditional Adversarial Networks. Proceeding of the IEEE International Conference on Computer Vision. 1125-1134. DOI: https://doi.org/10.1109/ISAPE.2012.6408893
Z. Yi, H. Zhang, P. Tan, and M. Gong. 2017. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation. Proceeding of the IEEE International Conference on Computer Vision. 2849-2857. DOI: https://doi.org/10.1109/ICCV.2017.310
N. Hamzah and F. H. Kamaru Zaman. 2020. Face Aging on Implementation Realistic Photo in Cross-Dataset. IOP Conference Series: Material Science and Engineering. 917(012080): 7. DOI: https://doi.org/10.1088/1757-899X/917/1/012080
I. J. Goodfellow. 2014. Generative Adversarial Networks. STAT. 1050:10. DOI: http://www.cs.utoronto.ca/~bonner/courses/2020s/csc2547/week5/GANs,-goodfellow,-nips2014.pdf
A. E. A. Zhu Jun-Yan, Taesung Park, Isola Phillip. 2010. Unpaired Image-to-Image Translation using Cycle-Consistent Network. Proceeding of the IEEE International Conference on Computer Vision. 183-202. DOI: https://doi.org/10.1007/978-1-60327-005-2_13
J. Song, J. Zhang, L. Gao, X. Liu, and H. T. Shen. 2017. Dual Conditional GANs for Face Aging and Rejuvenation. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. 899-905. DOI: https://doi.org/10.24963/ijcai.2018/125
Y. Choi, M. Choi, M. Kim, J. W. Ha, S. Kim, and J. Choo. 2018. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 8789-8797. DOI: https://doi.org/10.1109/CVPR.2018.00916
A. Heljakka, A. Solin, and J. Kannala. 2019. Pioneer Networks: Progressively Growing Generative Autoencoder. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 22-38. DOI: https://doi.org/10.1007/978-3-030-20887-5_2
C. Ledig et al. 2017. Photo-realistic single image super resolution using Generative Adversrial Network. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition. 105-114. DOI: https://doi.org/10.1109/CVPR.2017.19
T. O. Liu Ming-Yu. 2016. Coupled Generative Adversarial Networks. 30th Conference on Neural Information Processing System. 469-477. DOI: https://doi.org/10.1177/016173467900100106
E. Zhou, Z. Cao, and Q. Yin. 2015. Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?. arXiv preprint. arXiv:1501.04690. DOI: https://ui.adsabs.harvard.edu/link_gateway/2015arXiv150104690Z/arxiv:1501.04690
X. Zhu, X. Liu, Z. Lei, S. Z. Li, and H. Shi. 2016. Face Alignment Across Large Poses: A 3D Solution. Proceeding of the IEEE International Conference on Computer Vision and Pattern Recognition. 146-155. DOI: https://doi.org/10.1109/CVPR.2016.2
T. F. Cootes, C. J. Taylor, and A. Lanitis. 2013. Active Shape Models: Evaluation of a Multi-Resolution Method for Improving Image Search. In Proceeding of The British Machine Vision (BMVC). 1:32.1-32.10. DOI: https://doi.org/10.5244/C.8.32
D. Cristinacce and T. Cootes. 2007. Boosted Regression Active Shape Models. In Proceeding of The British Machine Vision (BMVC). 2:880-889. DOI: https://doi.org/10.5244/C.21.79
T. F. Cootes, G. J. Edwards, and C. J. Taylor. 2001. Active Appearance Models. Proceeding of the IEEE transactions on pattern analysis and machine intelligence. 23(6):681-685. DOI: https://doi.org/10.1007/978-981-10-7593-3_8
J. Matthews and S. Baker. 2004. Active appearance models revisited. International Journal of Computer Vision. 60(2):135-164. DOI: https://doi.org/10.1023/B:VISI.0000029666.37597.d3
N. Hamzah, F. H. Kamaru Zaman, and N. Md Tahir. 2021. Journal of Electrical & Electronic Systems Research. 19(OCT2021):7-16. DOI: https://doi.org/10.24191/jeesr.v19i1.002
S. Lucey, Y. Wang, M. Cox, S. Sridharan, and J. F. Cohn. 2009. Efficient constrained local model fitting for non-rigid face alignment. In the Image and Vision Computing. 27(12):1804-1813. DOI: https://doi.org/10.1016/j.imavis.2009.03.002
S. W. Chew, P. Lucey, S. Lucey, J. Saragih, J. F. Cohn, and S. Sridharan. 2011. Person-independent facial expression detection using Constrained Local Models. 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011. 915-920. DOI: https://doi.org/10.1109/FG.2011.5771373
Q. Liu, J. Deng, J. Yang, and G. Liu. 2016. Adaptive Cascade Regression Model for Robust Face Alignment. IEEE Transactions on Image Processing. 26(2):797-807. DOI: https://doi.org/10.1109/TIP.2016.2633939
F. Liu, D. Zeng, Q. Zhao, and X. Liu. 2016. Joint face alignment and 3D face reconstruction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9909 LNCS:545-560. DOI: https://doi.org/10.1007/978-3-319-46454-1_33
X. Cao, Y. Wei, F. Wen, and J. Sun. 2014. Face alignment by explicit shape regression. International journal of computer vision. 107(2):177-190. DOI: https://doi.org/10.1007/s11263-013-0667-3
S. Zhu, C. Li, C. C. Loy, and X. Tang. 2015. Face Alignment by Coarse-to-Fine Shape Searching. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 07-12-June:4998-5006. DOI: https://doi.org/10.1109/CVPR.2015.7299134
H. Yang, S. Member, X. He, X. Jia, and S. Member. 2015. Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation. Proceeding of IEEE Transaction on Image Processing. 24(8):2393-2403. DOI: https://doi.org/10.1109/TIP.2015.2421438
Q. Liu, J. Deng, and D. Tao. 2016. Dual Sparse Constrained Cascade Regression for Robust Face Alignment. Proceeding of IEEE Transaction on Image Processing. 25(2):700-712 DOI: https://doi.org/10.1109/TIP.2015.2502485
M. Kowalski, J. Naruniec, and T. Trzcinski. 2017. Deep Alignment Network : A Convolutional Neural Network for Robust Face Alignment. Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 88-97. DOI: https://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.254
W. Wu and S. Yang. 2017. Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment. Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 150-159. DOI: https://doi.org/10.1109/CVPRW.2017.261
H. Liu, J. Lu, S. Member, M. Guo, and S. Wu. 2020. Learning Reasoning-Decision Networks for Robust Face Alignment. Proceeding of IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(3):679-693. DOI: https://doi.org/10.1109/TPAMI.2018.2885298
P. Welander, S. Karlsson, and A. Eklund. 2018. Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT. Computing Research Repository. abs/1806.07777. DOI: https://doi.org/10.48550/arXiv.1806.07777
J. Y. Zhu et al. 2017. Toward multimodal image-to-image translation. Advances in Neural Information Processing Systems. 2017-Decem(1):466-477. DOI: https://doi.org/abs/10.5555/3294771.3294816
H. Zhang et al. 2017. StackGAN : Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. Proceeding of IEEE International Conference on Computer Vision. 5907-5915. DOI: https://doi.org/10.1109/ICCV.2017.629
S. Reed, Z. Akata, X. Yan, and L. Logeswaran. 2016. Generative Adversarial Text to Image Synthesis. Proceedings of The 33rd International Conference on Machine Learning. PMLR 48:1060-1069. DOI: https://doi.org/abs/10.5555/3045390.3045503
E. Pantraki and C. Kotropoulos. 2018. Face Aging as Image-to-Image Translation using Shared-Latent Space Generative Adversarial Networks. 2018 IEEE Global Conference on Signal and Information Processing. 306-310. DOI: https://doi.org/10.1109/GlobalSIP.2018.8646447
Z. Wang, X. Tang, W. Luo, and S. Gao. 2018. Face Aging with Identity-Preserved Conditional Generative Adversarial Networks. Proceeding of IEEE Conference on Computer Vision and Pattern Recognition. 7939-7947. DOI: https://doi.org/10.1109/CVPR.2018.00828
S. Liu et al. 2017. Face Aging with Contextual Generative Adversarial Nets. Proceedings of the 25th ACM international conference on Multimedia. 82-90. DOI: https://doi.org/10.1145/3123266.3123431
C. Han, H. Hayashi, L. Rundo, R. Araki, and W. Shimoda. 2018. GAN-Based Synthetic Brain MR Image Generation. 2018 IEEE 15th International Symposium on Biomedical Imaging. 734-738. DOI: https://doi.org/10.1109/ISBI.2018.8363678
C. Han, L. Rundo, R. Araki, and Y. Furukawa. 2018. Infinite Brain Tumor Images : Can GAN-based Data Augmentation Improve Tumor Detection on MR Images ?. In Proceeding Meeting on Image Recognition and Understanding. 1-4. DOI: http://133.11.169.134/pdf/MIRU_GAN.pdf
J. Johnson, A. Alahi, and L. Fei-fei. 2016. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. European Conference on Computer Vision. 9906:694-711. DOI: https://doi.org/10.1007/978-3-319-46475-6_43
M. Rosca, B. Lakshminarayanan, D. Warde-Farley, and S. Mohamed. 2017. Variational Approaches for Auto-Encoding Generative Adversarial Networks. arXiv preprint. arXiv:1706.04987. DOI: https://doi.org/10.48550/arXiv.1706.04987
A. Radford, L. Metz, and S. Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Lecture Notes in Computer Science. 10667:1-16. DOI: https://doi.org/10.1007/978-3-319-71589-6_9
P. Nagorny et al. 2019. Generative Adversarial Networks for geometric surfaces prediction in injection molding. 2018 IEEE International Conference on Industrial Technology. 1514-1519. DOI: https://doi.org/10.1109/ICIT.2018.8352405
A. Conneau, H. Schwenk, Y. Le Cun, and B. Loic. 2017. Very Deep Convolutional Networks for Text Classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. 1:1107-1116. DOI: https://doi.org/10.18653/v1/e17-1104
K. Kowsari, D. E. Brown, and M. Heidarysafa. 2017. HDLTex : Hierarchical Deep Learning for Text Classification. 2017 16th IEEE International Conference on Machine Learning and Applications. 364-371. DOI: https://doi.org/10.1109/ICMLA.2017.0-134
V. Singh, V. Shokeen, and B. Singh. 2013. Face Detection by Haar Cascade Classifier with Simple and Complex Background Images using OpenCV Implementation. International Journal of Advanced Technology in Engineering and Science. 1(12):33-38. DOI: http://www.ijates.com/images/short_pdf/1530959991_P33-38.pdf
P. I. Wilson and J. Fernandez. 2006. Facial feature detection using Haar Classifiers. Journal of computing sciences in colleges. 21(4):127-133. DOI: https://doi.org/abs/10.5555/1127389.1127416
