skip to main content
research-article

Matching Faces and Attributes Between the Artistic and the Real Domain: the PersonArt Approach

Published:04 March 2022Publication History
Skip Abstract Section

Abstract

In this article, we present an approach for retrieving similar faces between the artistic and the real domain. The application we refer to is an interactive exhibition inside a museum, in which a visitor can take a photo of himself and search for a lookalike in the collection of paintings. The task requires not only to identify faces but also to extract discriminative features from artistic and photo-realistic images, tackling a significant domain shift. Our method integrates feature extraction networks which account for the aesthetic similarity of two faces and their correspondences in terms of semantic attributes. Also, it addresses the domain shift between realistic images and paintings by translating photo-realistic images into the artistic domain. Noticeably, by exploiting the same technique, our model does not need to rely on annotated data in the artistic domain. Experimental results are conducted on different paired datasets to show the effectiveness of the proposed solution in terms of identity and attribute preservation. The approach is also evaluated on unpaired settings and in combination with an interactive relevance feedback strategy. Finally, we show how the proposed algorithm has been implemented in a real showcase at the Gallerie Estensi museum in Italy, with the participation of more than 1,100 visitors in just three days.

REFERENCES

  1. [1] Abaci Bahri and Akgul Tayfun. 2015. Matching caricatures to photographs. Signal, Image and Video Processing 9, 1 (2015), 295303.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Anjomshoae Sule, Najjar Amro, Calvaresi Davide, and Främling Kary. 2019. Explainable agents and robots: Results from a systematic literature review. In Proceedings of the International Conference on Autonomous Agents and MultiAgent Systems.Google ScholarGoogle Scholar
  3. [3] Anoosheh Asha, Agustsson Eirikur, Timofte Radu, and Gool Luc Van. 2018. ComboGAN: Unrestrained scalability for image domain translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Aubry Mathieu, Russell Bryan C., and Sivic Josef. 2014. Painting-to-3D model alignment via discriminative visual elements. ACM Transactions on Graphics 33, 2 (2014), 14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Baraldi Lorenzo, Cornia Marcella, Grana Costantino, and Cucchiara Rita. 2018. Aligning text and document illustrations: Towards visually explainable digital humanities. In Proceedings of the International Conference on Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Bhatt Himanshu S., Bharadwaj Samarth, Singh Richa, and Vatsa Mayank. 2012. Memetically optimized MCWLD for matching sketches with digital face images. IEEE Transactions on Information Forensics and Security 7, 5 (2012), 15221535.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Bongini Pietro, Becattini Federico, Bagdanov Andrew D., and Bimbo Alberto Del. 2020. Visual question answering for cultural heritage. IOP Conference Series: Materials Science and Engineering 949, 1 (2020), 012074.Google ScholarGoogle Scholar
  8. [8] Borghesani Daniele, Grana Costantino, and Cucchiara Rita. 2014. Miniature illustrations retrieval and innovative interaction for digital illuminated manuscripts. Multimedia Systems 20, 1 (2014), 6579.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. [9] Bourdev Lubomir, Maji Subhransu, and Malik Jitendra. 2011. Describing people: A poselet-based approach to attribute classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Cao Qiong, Shen Li, Xie Weidi, Parkhi Omkar M, and Zisserman Andrew. 2018. VGGFace2: A dataset for recognising faces across pose and age. In Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. [11] Carraggi Angelo, Cornia Marcella, Baraldi Lorenzo, and Cucchiara Rita. 2018. Visual-semantic alignment across domains using a semi-supervised approach. In Proceedings of the European Conference on Computer Vision Workshops.Google ScholarGoogle Scholar
  12. [12] Cascianelli Silvia, Cornia Marcella, Baraldi Lorenzo, Piazzi Maria Ludovica, Schiuma Rosiana, and Cucchiara Rita. 2021. Learning to read l’infinito: Handwritten text recognition with synthetic training data. In Proceedings of the International Conference on Computer Analysis of Images and Patterns.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Castellano Giovanna, Lella Eufemia, and Vessio Gennaro. 2021. Visual link retrieval and knowledge discovery in painting datasets. Multimedia Tools and Applications 80, 5 (2021), 65996616.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Castellano Giovanna and Vessio Gennaro. 2021. Deep learning approaches to pattern extraction and recognition in paintings and drawings: An overview. Neural Computing and Applications 33, 6 (2021), 120.Google ScholarGoogle Scholar
  15. [15] Chang Yao-Jen, Kamataki Keisuke, and Chen Tsuhan. 2009. Mean shift feature space warping for relevance feedback. In Proceedings of the IEEE International Conference on Image Processing.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Chen Dongdong, Yuan Lu, Liao Jing, Yu Nenghai, and Hua Gang. 2017. Stylebank: An explicit representation for neural image style transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Choi Yunjey, Choi Minje, Kim Munyoung, Ha Jung-Woo, Kim Sunghun, and Choo Jaegul. 2018. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Cornia Marcella, Stefanini Matteo, Baraldi Lorenzo, Corsini Massimiliano, and Cucchiara Rita. 2020. Explaining digital humanities by aligning images and textual descriptions. Pattern Recognition Letters 129 (2020), 166172. https://www.sciencedirect.com/science/article/pii/S0167865519303381.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Crowley Elliot J., Parkhi Omkar M., and Zisserman Andrew. 2015. Face painting: Querying art with photos. In Proceedings of the British Machine Vision Conference.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Crowley Elliot J. and Zisserman Andrew. 2014. The state of the art: Object retrieval in paintings using discriminative regions. In Proceedings of the British Machine Vision Conference.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Crowley Elliot J. and Zisserman Andrew. 2016. The art of detection. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Chiaro Riccardo Del, Bagdanov Andrew D., and Bimbo Alberto Del. 2019. Webly-supervised zero-shot learning for artwork instance recognition. Pattern Recognition Letters 128, 2 (2019), 420426.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Deng Jiankang, Guo Jia, Xue Niannan, and Zafeiriou Stefanos. 2019. ArcFace: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Ding Changxing and Tao Dacheng. 2015. Robust face recognition via multimodal deep face representation. IEEE Transactions on Multimedia 17, 11 (2015), 20492058.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Faghri Fartash, Fleet David J., Kiros Jamie Ryan, and Fidler Sanja. 2018. VSE++: Improving visual-semantic embeddings with hard negatives. In Proceedings of the British Machine Vision Conference.Google ScholarGoogle Scholar
  26. [26] Garcia Noa and Vogiatzis George. 2018. How to read paintings: Semantic art understanding with multi-modal retrieval. In Proceedings of the European Conference on Computer Vision Workshops.Google ScholarGoogle Scholar
  27. [27] Gatys Leon A., Ecker Alexander S., and Bethge Matthias. 2016. Image style transfer using convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Gatys Leon A., Ecker Alexander S., Bethge Matthias, Hertzmann Aaron, and Shechtman Eli. 2017. Controlling perceptual factors in neural style transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Ghiasi Golnaz, Lee Honglak, Kudlur Manjunath, Dumoulin Vincent, and Shlens Jonathon. 2017. Exploring the structure of a real-time, arbitrary neural artistic stylization network. In Proceedings of the British Machine Vision Conference.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Ginosar Shiry, Haas Daniel, Brown Timothy, and Malik Jitendra. 2014. Detecting people in cubist art. In Proceedings of the European Conference on Computer Vision Workshops.Google ScholarGoogle Scholar
  31. [31] Günther Manuel, Rozsa Andras, and Boult Terranee E.. 2017. AFFACT: Alignment-free facial attribute classification technique. In Proceeding of the International Joint Conference on Biometrics.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Guo Yandong, Zhang Lei, Hu Yuxiao, He Xiaodong, and Gao Jianfeng. 2016. MS-celeb-1M: A dataset and benchmark for large-scale face recognition. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Hand Emily M. and Chellappa Rama. 2017. Attributes for improved attributes: A multi-task network utilizing implicit and explicit relationships for facial attribute classification. In Proceedings of the AAAI Conference on Artificial Intelligence.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] He Kaiming, Zhang Xiangyu, Ren Shaoqing, and Sun Jian. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Hu Jie, Shen Li, and Sun Gang. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Huang Xun, Liu Ming-Yu, Belongie Serge, and Kautz Jan. 2018. Multimodal unsupervised image-to-image translation. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Huo Jing, Gao Yang, Shi Yinghuan, and Yin Hujun. 2017. Variation robust cross-modal metric learning for caricature recognition. In Proceedings of the ACM International Conference on Multimedia Workshops.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. [38] Huo Jing, Li Wenbin, Shi Yinghuan, Gao Yang, and Yin Hujun. 2018. WebCaricature: A benchmark for caricature face recognition. In Proceedings of the British Machine Vision Conference.Google ScholarGoogle Scholar
  39. [39] Isola Phillip, Zhu Jun-Yan, Zhou Tinghui, and Efros Alexei A.. 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Jing Yongcheng, Liu Yang, Yang Yezhou, Feng Zunlei, Yu Yizhou, Tao Dacheng, and Song Mingli. 2018. Stroke controllable fast style transfer with adaptive receptive fields. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Johnson Justin, Alahi Alexandre, and Fei-Fei Li. 2016. Perceptual losses for real-time style transfer and super-resolution. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Karayev Sergey, Trentacoste Matthew, Han Helen, Agarwala Aseem, Darrell Trevor, Hertzmann Aaron, and Winnemoeller Holger. 2014. Recognizing image style. In Proceedings of the British Machine Vision Conference.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Kingma Diederik P. and Ba Jimmy. 2015. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  44. [44] Klare Brendan F., Bucak Serhat S., Jain Anil K., and Akgul Tayfun. 2012. Towards automated caricature recognition. In Proceedings of the International Conference on Biometrics.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Kumar Neeraj, Berg Alexander C., Belhumeur Peter N., and Nayar Shree K.. 2009. Attribute and simile classifiers for face verification. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Ledig Christian, Theis Lucas, Huszar Ferenc, Caballero Jose, Cunningham Andrew, Acosta Alejandro, Aitken Andrew, Tejani Alykhan, Totz Johannes, Wang Zehan, and Shi Wenzhe. 2017. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Lee Hsin-Ying, Tseng Hung-Yu, Huang Jia-Bin, Singh Maneesh Kumar, and Yang Ming-Hsuan. 2018. Diverse image-to-image translation via disentangled representations. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Li Chuan and Wand Michael. 2016. Precomputed real-time texture synthesis with markovian generative adversarial networks. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Li Yijun, Fang Chen, Yang Jimei, Wang Zhaowen, Lu Xin, and Yang Ming-Hsuan. 2017. Diversified texture synthesis with feed-forward networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Li Yijun, Fang Chen, Yang Jimei, Wang Zhaowen, Lu Xin, and Yang Ming-Hsuan. 2017. Universal style transfer via feature transforms. In Proceedings of the Advances in Neural Information Processing Systems.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Liu Ming-Yu, Breuel Thomas, and Kautz Jan. 2017. Unsupervised image-to-image translation networks. In Proceedings of the Advances in Neural Information Processing Systems.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Liu Weiyang, Wen Yandong, Yu Zhiding, Li Ming, Raj Bhiksha, and Song Le. 2017. Sphereface: Deep hypersphere embedding for face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Liu Weiyang, Wen Yandong, Yu Zhiding, and Yang Meng. 2016. Large-margin softmax loss for convolutional neural networks. In Proceedings of the International Conference on Machine Learning.Google ScholarGoogle Scholar
  54. [54] Liu Ziwei, Luo Ping, Wang Xiaogang, and Tang Xiaoou. 2015. Deep learning face attributes in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. [55] Lu Yongxi, Kumar Abhishek, Zhai Shuangfei, Cheng Yu, Javidi Tara, and Feris Rogerio. 2017. Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Ma Shuang, Fu Jianlong, Chen Chang Wen, and Mei Tao. 2018. DA-GAN: Instance-level image translation by deep attention generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Malisiewicz Tomasz, Gupta Abhinav, and Efros Alexei A.. 2011. Ensemble of exemplar-SVMs for object detection and beyond. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. [58] Mao Hui, Cheung Ming, and She James. 2017. Deepart: Learning joint representations of visual arts. In Proceedings of the ACM International Conference on Multimedia.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Masi Iacopo, Wu Yue, Hassner Tal, and Natarajan Prem. 2018. Deep face recognition: A survey. In Proceedings of the Conference on Graphics, Patterns and Images.Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Mathieu Michael, Couprie Camille, and LeCun Yann. 2016. Deep multi-scale video prediction beyond mean square error. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  61. [61] Mishra Ashutosh, Rai Shyam Nandan, Mishra Anand, and Jawahar CV. 2016. IIIT-CFW: A benchmark database of cartoon faces in the wild. In Proceedings of the European Conference on Computer Vision Workshops.Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Parkhi Omkar M., Vedaldi Andrea, and Zisserman Andrew. 2015. Deep face recognition. In Proceedings of the British Machine Vision Conference.Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Pathak Deepak, Krahenbuhl Philipp, Donahue Jeff, Darrell Trevor, and Efros Alexei A.. 2016. Context encoders: Feature learning by inpainting. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Picard David, Gosselin Philippe-Henri, and Gaspard Marie-Claude. 2015. Challenges in content-based image indexing of cultural heritage collections. IEEE Signal Processing Magazine 32, 4 (2015), 95102.Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Reed Scott, Akata Zeynep, Yan Xinchen, Logeswaran Lajanugen, Schiele Bernt, and Lee Honglak. 2016. Generative adversarial text to image synthesis. In Proceedings of the International Conference on Machine Learning.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. [66] Reed Scott E., Akata Zeynep, Mohan Santosh, Tenka Samuel, Schiele Bernt, and Lee Honglak. 2016. Learning what and where to draw. In Proceedings of the Advances in Neural Information Processing Systems.Google ScholarGoogle Scholar
  67. [67] Rudd Ethan M., Günther Manuel, and Boult Terrance E.. 2016. MOON: A mixed objective optimization network for the recognition of facial attributes. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  68. [68] Sanakoyeu Artsiom, Kotovenko Dmytro, Lang Sabine, and Ommer Björn. 2018. A style-aware content loss for real-time hd style transfer. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  69. [69] Sankaranarayanan Swami, Alavi Azadeh, Castillo Carlos D., and Chellappa Rama. 2016. Triplet probabilistic embedding for face verification and clustering. In Proceedings of the International Conference on Biometrics Theory, Applications and Systems.Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. [70] Schroff Florian, Kalenichenko Dmitry, and Philbin James. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  71. [71] Selvaraju Ramprasaath R., Cogswell Michael, Das Abhishek, Vedantam Ramakrishna, Parikh Devi, and Batra Dhruv. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  72. [72] Shen Falong, Yan Shuicheng, and Zeng Gang. 2018. Neural style transfer via meta networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  73. [73] Shen Xi, Efros Alexei A., and Aubry Mathieu. 2019. Discovering visual patterns in art collections with spatially-consistent feature learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  74. [74] Simonyan Karen, Vedaldi Andrea, and Zisserman Andrew. 2014. Deep inside convolutional networks: Visualising image classification models and saliency maps. In Proceedings of the International Conference on Learning Representations Workshops.Google ScholarGoogle Scholar
  75. [75] Simonyan Karen and Zisserman Andrew. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  76. [76] Springenberg Jost Tobias, Dosovitskiy Alexey, Brox Thomas, and Riedmiller Martin. 2015. Striving for simplicity: The all convolutional net. In Proceedings of the International Conference on Learning Representations Workshops.Google ScholarGoogle Scholar
  77. [77] Stefanini Matteo, Cornia Marcella, Baraldi Lorenzo, Corsini Massimiliano, and Cucchiara Rita. 2019. Artpedia: A new visual-semantic dataset with visual and contextual sentences in the artistic domain. In Proceedings of the International Conference on Image Analysis and Processing.Google ScholarGoogle ScholarDigital LibraryDigital Library
  78. [78] Strezoski Gjorgji and Worring Marcel. 2017. OmniArt: Multi-task deep learning for artistic data analysis. ACM Transactions on Multimedia Computing, Communications, and Applications 14, 4 (2017), 88:1–88:21.Google ScholarGoogle Scholar
  79. [79] Sun Yi, Chen Yuheng, Wang Xiaogang, and Tang Xiaoou. 2014. Deep learning face representation by joint identification-verification. In Proceedings of the Advances in Neural Information Processing Systems.Google ScholarGoogle Scholar
  80. [80] Sun Yi, Liang Ding, Wang Xiaogang, and Tang Xiaoou. 2015. DeepID3: Face recognition with very deep neural networks. arXiv:1502.00873. Retrieved from https://arxiv.org/abs/1502.00873.Google ScholarGoogle Scholar
  81. [81] Sun Yi, Wang Xiaogang, and Tang Xiaoou. 2014. Deep learning face representation from predicting 10,000 classes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. [82] Sundararajan Mukund, Taly Ankur, and Yan Qiqi. 2017. Axiomatic attribution for deep networks. In Proceedings of the International Conference on Machine Learning.Google ScholarGoogle Scholar
  83. [83] Taigman Yaniv, Polyak Adam, and Wolf Lior. 2017. Unsupervised cross-domain image generation. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  84. [84] Taigman Yaniv, Yang Ming, Ranzato Marc’Aurelio, and Wolf Lior. 2014. DeepFace: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarDigital LibraryDigital Library
  85. [85] Tomei Matteo, Baraldi Lorenzo, Cornia Marcella, and Cucchiara Rita. 2018. What was monet seeing while painting? Translating artworks to photo-realistic images. In Proceedings of the European Conference on Computer Vision Workshops.Google ScholarGoogle Scholar
  86. [86] Tomei Matteo, Cornia Marcella, Baraldi Lorenzo, and Cucchiara Rita. 2019. Art2Real: Unfolding the reality of artworks via semantically-aware image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  87. [87] Tomei Matteo, Cornia Marcella, Baraldi Lorenzo, and Cucchiara Rita. 2019. Image-to-image translation to unfold the reality of artworks: An empirical analysis. In Proceedings of the International Conference on Image Analysis and Processing.Google ScholarGoogle ScholarDigital LibraryDigital Library
  88. [88] Ulyanov Dmitry, Lebedev Vadim, Vedaldi Andrea, and Lempitsky Victor S.. 2016. Texture networks: Feed-forward synthesis of textures and stylized images. In Proceedings of the International Conference on Machine Learning.Google ScholarGoogle Scholar
  89. [89] Ulyanov Dmitry, Vedaldi Andrea, and Lempitsky Victor S.. 2017. Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  90. [90] Walker Jacob, Marino Kenneth, Gupta Abhinav, and Hebert Martial. 2017. The pose knows: Video forecasting by generating pose futures. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  91. [91] Wang Fei, Chen Liren, Li Cheng, Huang Shiyao, Chen Yanjie, Qian Chen, and Loy Chen Change. 2018. The devil of face recognition is in the noise. In Proceedings of the European Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  92. [92] Wang Feng, Cheng Jian, Liu Weiyang, and Liu Haijun. 2018. Additive margin softmax for face verification. IEEE Signal Processing Letters 25, 7 (2018), 926930.Google ScholarGoogle ScholarCross RefCross Ref
  93. [93] Wang Mei and Deng Weihong. 2018. Deep face recognition: A survey. Neurocomputing 429 (2021), 215–244.Google ScholarGoogle Scholar
  94. [94] Westlake Nicholas, Cai Hongping, and Hall Peter. 2016. Detecting people in artwork with CNNs. In Proceedings of the European Conference on Computer Vision Workshops.Google ScholarGoogle ScholarCross RefCross Ref
  95. [95] Wilber Michael J., Fang Chen, Jin Hailin, Hertzmann Aaron, Collomosse John, and Belongie Serge. 2017. Bam! the behance artistic media dataset for recognition beyond photography. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  96. [96] Wu Xiang, He Ran, Sun Zhenan, and Tan Tieniu. 2018. A light CNN for deep face representation with noisy labels. IEEE Transactions on Information Forensics and Security 13, 11 (2018), 28842896.Google ScholarGoogle ScholarCross RefCross Ref
  97. [97] Yang Xuewen, Xie Dongliang, and Wang Xin. 2018. Crossing-domain generative adversarial networks for unsupervised multi-domain image-to-image translation. In Proceedings of the ACM International Conference on Multimedia.Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. [98] Yeh Raymond A., Chen Chen, Lim Teck-Yian, Schwing Alexander G., Hasegawa-Johnson Mark, and Do Minh N.. 2017. Semantic image inpainting with deep generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  99. [99] Zhang Shifeng, Zhu Xiangyu, Lei Zhen, Shi Hailin, Wang Xiaobo, and Li Stan Z. 2017. S3fd: Single shot scale-invariant face detector. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle Scholar
  100. [100] Zheng Xin, Guo Yanqing, Huang Huaibo, Li Yi, and He Ran. 2020. A survey of deep facial attribute analysis. International Journal of Computer Vision 128, 8 (2020), 133.Google ScholarGoogle Scholar
  101. [101] Zhong Yang, Sullivan Josephine, and Li Haibo. 2016. Face attribute prediction using off-the-shelf CNN features. In Proceedings of the International Conference on Biometrics.Google ScholarGoogle ScholarCross RefCross Ref
  102. [102] Zhu Ciyou, Byrd Richard H., Lu Peihuang, and Nocedal Jorge. 1997. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software 23, 4 (1997), 550560.Google ScholarGoogle ScholarDigital LibraryDigital Library
  103. [103] Zhu Jun-Yan, Park Taesung, Isola Phillip, and Efros Alexei A.. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref
  104. [104] Zhu Jun-Yan, Zhang Richard, Pathak Deepak, Darrell Trevor, Efros Alexei A., Wang Oliver, and Shechtman Eli. 2017. Toward multimodal image-to-image translation. In Proceedings of the Advances in Neural Information Processing Systems.Google ScholarGoogle Scholar

Index Terms

  1. Matching Faces and Attributes Between the Artistic and the Real Domain: the PersonArt Approach

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 3
          August 2022
          478 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3505208
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 4 March 2022
          • Accepted: 1 October 2021
          • Revised: 1 September 2021
          • Received: 1 December 2020
          Published in tomm Volume 18, Issue 3

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Refereed
        • Article Metrics

          • Downloads (Last 12 months)111
          • Downloads (Last 6 weeks)5

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!