Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 8-9/2020

24.03.2020

A Survey of Deep Facial Attribute Analysis

verfasst von: Xin Zheng, Yanqing Guo, Huaibo Huang, Yi Li, Ran He

Erschienen in: International Journal of Computer Vision | Ausgabe 8-9/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this two-stage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.
Vorheriger Artikel Efficient Visual Recognition
Nächster Artikel Hardware-Centric AutoML for Mixed-Precision Quantization

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
1
Amazon Mechanical Turk. https://​www.​mturk.​com/​.
 
Literatur
Belghazi, M. I., Rajeswar, S., Mastropietro, O., Rostamzadeh, N., Mitrovic, J., & Courville, A. (2018). Hierarchical adversarially learned inference. arXiv preprint arXiv:​1802.​01071.
Berg, T., & Belhumeur, P. N. (2013). Poof: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 955–962). IEEE.
Berthelot, D., Raffel, C., Roy, A., & Goodfellow, I. (2019). Understanding and improving interpolation in autoencoders via an adversarial regularizer. In Proceedings of the international conference on learning representations (ICLR).
Bourdev, L., Maji, S., & Malik, J. (2011). Describing people: A poselet-based approach to attribute classification. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 1543–1550). IEEE.
Bourdev, L., & Malik, J. (2009). Poselets: Body part detectors trained using 3d human pose annotations. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 1365–1372). IEEE.
Cao, C., Lu, F., Li, C., Lin, S., & Shen, X. (2019a). Makeup removal via bidirectional tunable de-makeup network. IEEE Transactions on Multimedia, 21, 2750–2761.CrossRef
Cao, J., Hu, Y., Yu, B., He, R., & Sun, Z. (2019b). 3D aided duet gans for multi-view face image synthesis. IEEE Transactions on Information Forensics and Security (TIFS), 14(8), 2028–2042.CrossRef
Cao, J., Li, Y., & Zhang, Z. (2018). Partially shared multi-task convolutional neural network with local constraint for face attribute learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 4290–4299).
Chang, H., Lu, J., Yu, F., & Finkelstein, A. (2018). Pairedcyclegan: Asymmetric style transfer for applying and removing makeup. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 40–48).
Chen, J. C., Ranjan, R., Sankaranarayanan, S., Kumar, A., Chen, C. H., Patel, V. M., et al. (2018). Unconstrained still/video-based face verification with deep convolutional neural networks. International Journal of Computer Vision (IJCV), 126(2–4), 272–291.MathSciNetCrossRef
Chen, L., Zhang, Q., & Li, B. (2014). Predicting multiple attributes via relative multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1027–1034).
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems (NIPS) (pp. 2172–2180).
Chen, Y. C., Shen, X., Lin, Z., Lu, X., Pao, I., Jia, J., et al. (2019). Semantic component decomposition for face attribute manipulation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 9859–9867).
Chhabra, S., Singh, R., Vatsa, M., & Gupta, G. (2018) Anonymizing k-facial attributes via adversarial perturbations. In Proceedings of the international joint conference on artificial intelligence (IJCAI) (pp. 656–662).
Choi, Y., Choi, M., & Kim, M. (2018). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 8789–8797).
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.MATH
Ding, H., Zhou, H., Zhou, S. K., & Chellappa, R. (2018). A deep cascade network for unaligned face attribute classification. In Proceedings of the conference on artificial intelligence (AAAI).
Dong, Q., Gong, S., & Zhu, X. (2017). Class rectification hard mining for imbalanced deep learning. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 1869–1878). IEEE.
Dorta, G., Vicente, S., Campbell, N. D., & Simpson, I. (2018). The GAN that warped: Semantic attribute editing with unpaired data. arXiv preprint arXiv:​1811.​12784.
Egger, B., Schönborn, S., Schneider, A., Kortylewski, A., Morel-Forster, A., Blumer, C., et al. (2018). Occlusion-aware 3d morphable models and an illumination prior for face image analysis. International Journal of Computer Vision (IJCV), 126, 1269–1287.CrossRef
Fan, Q., Gabbur, P., & Pankanti, S. (2013). Relative attributes for large-scale abandoned object detection. In Proceedings of the IEEE International conference on computer vision (ICCV) (pp. 2736–2743).
Fang, Y., & Yuan, Q. (2018). Attribute-enhanced metric learning for face retrieval. EURASIP Journal on Image and Video Processing, 2018(1), 44.CrossRef
Fathy, M. E., Patel, V. M., & Chellappa, R. (2015). Face-based active authentication on mobile devices. In Proceedings of the IEEE conference on acoustics, speech and signal processing (ICASSP) (pp. 1687–1691). IEEE.
Fukui, H., Hirakawa, T., Yamashita, T., & Fujiyoshi, H. (2019). Attention branch network: Learning of attention mechanism for visual explanation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 10705–10714).
Gkioxari, G., Girshick, R., & Malik, J. (2015). Actions and attributes from wholes and parts. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 2470–2478). IEEE.
Gonzalez-Garcia, A., Modolo, D., & Ferrari, V. (2018). Do semantic parts emerge in convolutional neural networks? International Journal of Computer Vision (IJCV), 126(5), 476–494.MathSciNetCrossRef
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014). Generative adversarial nets. In Advances in neural information processing systems (NIPS) (pp. 2672–2680).
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. In Proceedings of the international conference on learning representations (ICLR).
Günther, M., Costa-Pazo, A., Ding, C., Boutellaa, E., Chiachia, G., Zhang, H., et al. (2013). The 2013 face recognition evaluation in mobile environment. In Proceedings of the international conference on biometrics (ICB) (pp. 1–7). IEEE.
Günther, M., Rozsa, A., & Boult, T. E. (2017). AFFACT: Alignment-free facial attribute classification technique. In Proceedings of the IEEE international joint conference on biometrics (IJCB) (pp. 90–99). IEEE.
Hadid, A., Heikkila, J., Silvén, O., & Pietikainen, M. (2007). Face and eye detection for person authentication in mobile phones. In Proceedings of the ACM/IEEE international conference on distributed smart cameras (pp. 101–108). IEEE.
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications, 73, 220–239.CrossRef
Han, H., Jain, A. K., Shan, S., & Chen, X. (2017). Heterogeneous face attribute estimation: A deep multi-task learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40, 2597–2609.CrossRef
Hand, E. M., Castillo, C. D., & Chellappa, R. (2018a). Doing the best we can with what we have: Multi-label balancing with selective learning for attribute prediction. In Proceedings of the conference on artificial intelligence (AAAI) (pp. 6878–6885).
Hand, E. M., Castillo, C. D., & Chellappa, R. (2018b). Predicting facial attributes in video using temporal coherence and motion-attention. In Proceedings of the IEEE winter conference on applications of computer vision (WACV) (pp. 84–92). IEEE.
Hand, E. M., & Chellappa, R. (2017). Attributes for improved attributes: A multi-task network utilizing implicit and explicit relationships for facial attribute classification. In Proceedings of the conference on artificial intelligence (AAAI) (pp. 4068–4074).
He, D., Xia, Y., Qin, T., Wang, L., Yu, N., Liu, T., & Ma, W. Y. (2016a). Dual learning for machine translation. In Advances in neural information processing systems (NIPS) (pp. 820–828).
He, K., Fu, Y., & Xue, X. (2017). A jointly learned deep architecture for facial attribute analysis and face detection in the wild. arXiv preprint arXiv:​1707.​08705.
He, K., Fu, Y., Zhang, W., Wang, C., Jiang, Y. G., Huang, F., & Xue, X. (2018a). Harnessing synthesized abstraction images to improve facial attribute recognition. In Proceedings of the international joint conference on artificial intelligence (IJCAI).
He, K., Zhang, X., Ren, S., & Sun, J. (2016b). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 770–778).
He, R., Tan, T., Davis, L., & Sun, Z. (2018b). Learning structured ordinal measures for video based face recognition. Pattern Recognition, 75, 4–14.CrossRef
He, R., Wu, X., Sun, Z., & Tan, T. (2018c). Wasserstein CNN: Learning invariant features for NIR-VIS face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41, 1761–1773.CrossRef
He, Z., Zuo, W., Kan, M., Shan, S., & Chen, X. (2019). Attgan: Facial attribute editing by only changing what you want. IEEE Transactions on Image Processing (TIP), 28, 5464–5478.MathSciNetMATHCrossRef
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in neural information processing systems (NIPS) (pp. 6626–6637).
Hu, Y., Wu, X., Yu, B., He, R., & Sun, Z. (2018). Pose-guided photorealistic face rotation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).
Huang, C., Li, Y., Change Loy, C., & Tang, X. (2016). Learning deep representation for imbalanced classification. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5375–5384).
Huang, G. B., Mattar, M., Berg, T., & Learned-Miller, E. (2008). Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in ‘Real-Life’ images: Detection, alignment, and recognition.
Huang, H., He, R., Sun, Z., Tan, T., et al. (2018a). Introvae: Introspective variational autoencoders for photographic image synthesis. In Advances in neural information processing systems (NIPS) (pp. 52–63).
Huang, H., Song, L., He, R., Sun, Z., & Tan, T. (2018b). Variational capsules for image analysis and synthesis. arXiv preprint arXiv:​1807.​04099.
Kalayeh, M. M., Gong, B., & Shah, M. (2017). Improving facial attribute prediction using semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 4227–4235). IEEE.
Kazemi, V., & Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1867–1874).
Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. In Proceedings of the international conference on learning representations (ICLR).
Kumar, N., Belhumeur, P., & Nayar, S. (2008). Facetracer: A search engine for large collections of images with faces. In Proceedings of the European conference on computer vision (ECCV) (pp. 340–353). Springer.
Kumar, N., Berg, A., Belhumeur, P. N., & Nayar, S. (2011). Describable visual attributes for face verification and image search. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(10), 1962–1977.CrossRef
Kumar, N., Berg, A. C., Belhumeur, P. N., & Nayar, S. K. (2009). Attribute and simile classifiers for face verification. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 365–372). IEEE.
Lampert, C. H., Nickisch, H., & Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 951–958). IEEE.
Lample, G., Zeghidour, N., Usunier, N., Bordes, A., Denoyer, L., et al. (2017). Fader networks: Manipulating images by sliding attributes. In Advances in neural information processing systems (NIPS) (pp. 5967–5976).
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2016). Autoencoding beyond pixels using a learned similarity metric. In Proceedings of the IEEE international conference on machine learning (ICML) (pp. 1558–1566).
Le, V., Brandt, J., Lin, Z., Bourdev, L., & Huang, T. S. (2012). Interactive facial feature localization. In Proceedings of the European conference on computer vision (ECCV) (pp. 679–692). Springer.
Li, H. Y., Dong, W. M., & Hu, B. G. (2018a). Facial image attributes transformation via conditional recycle generative adversarial networks. Journal of Computer Science and Technology (JCST), 33(3), 511–521.CrossRef
Li, J., Zhao, F., Feng, J., Roy, S., Yan, S., & Sim, T. (2018b). Landmark free face attribute prediction. IEEE Transactions on Image Processing (TIP), 27(9), 4651–4662.MathSciNetCrossRef
Li, T., Qian, R., Dong, C., Liu, S., Yan, Q., Zhu, W., & Lin, L. (2018c). Beautygan: Instance-level facial makeup transfer with deep generative adversarial network. In Proceedings of the ACM multimedia conference on multimedia conference (ACMMM) (pp. 645–653). ACM.
Li, Y., Wang, R., Liu, H., Jiang, H., Shan, S., & Chen, X. (2015). Two birds, one stone: Jointly learning binary code for large-scale face image retrieval and attributes prediction. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 3819–3827). IEEE.
Liu, A. H., Liu, Y. C., Yeh, Y. Y., & Wang, Y. C. F. (2018). A unified feature disentangler for multi-domain image translation and manipulation. In Advances in neural information processing systems (NIPS) (pp. 2591–2600).
Liu, M. Y., Breuel, T., & Kautz, J. (2017). Unsupervised image-to-image translation networks. In Advances in neural information processing systems (NIPS) (pp. 700–708).
Liu, Y., Li, Q., & Sun, Z. (2019). Attribute-aware face aging with wavelet-based generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 11877–11886).
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 3730–3738).
Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., & Feris, R. (2017). Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (Vol. 1, p. 6).
Lu, Y., Tai, Y. W., & Tang, C. K. (2018a) Attribute-guided face generation using conditional cyclegan. In Proceedings of the European conference on computer vision (ECCV) (pp. 293–308). Springer.
Lu, Z., Hu, T., Song, L., Zhang, Z., & He, R. (2018b). Conditional expression synthesis with face parsing transformation. In Proceedings of the ACM international conference on multimedia (ACMMM) (pp. 1083–1091). ACM.
Luo, P., Wang, X., & Tang, X. (2013). A deep sum-product architecture for robust facial attributes analysis. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 2864–2871). IEEE.
Ma, L., Jia, X., Georgoulis, S., Tuytelaars, T., & Van Gool, L. (2018). Exemplar guided unsupervised image-to-image translation with semantic consistency. In Proceedings of the international conference on learning representations (ICLR).
Meng, Z., Adluru, N., Kim, H. J., Fung, G., & Singh, V. (2018). Efficient relative attribute learning using graph neural networks. In Proceedings of the European conference on computer vision (ECCV) (pp. 552–567).
Miller, T. L., Berg, A. C., Edwards, J. A., Maire, M. R., White, R. M., Teh, Y. W., et al. (2007). Names and faces.
Nguyen, H. M., Ly, N. Q., & Phung, T. T. (2018). Large-scale face image retrieval system at attribute level based on facial attribute ontology and deep neuron network. In Asian conference on intelligent information and database systems (pp. 539–549). Springer.
Nhan Duong, C., Luu, K., Gia Quach, K., Nguyen, N., Patterson, E., Bui, T. D., Le, N. (2019). Automatic face aging in videos via deep reinforcement learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 10013–10022).
Parikh, D., & Grauman, K. (2011). Relative attributes. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 503–510). IEEE.
Parkhi, O. M., Vedaldi, A., Zisserman, A., et al. (2015). Deep face recognition. In Proceedings of the British machine vision conference 2015, (BMVC) (Vol. 1, p. 6).
Perarnau, G., van de Weijer, J., Raducanu, B., & Álvarez, J. M. (2016). Invertible conditional gans for image editing. In Advances in neural information processing systems workshop on adversarial training (NIPSW).
Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2007). Object retrieval with large vocabularies and fast spatial matching. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1–8). IEEE.
Ranjan, R., Patel, V. M., & Chellappa, R. (2017). Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41, 121–135.CrossRef
Ranjan, R., Sankaranarayanan, S., Castillo, C. D., & Chellappa, R. (2017). An all-in-one convolutional neural network for face analysis. In Proceedings of the IEEE international conference on automatic face & gesture recognition (FG) (pp. 17–24). IEEE.
Rao, Y., Lu, J., & Zhou, J. (2018). Learning discriminative aggregation network for video-based face recognition and person re-identification. International Journal of Computer Vision (IJCV), 127, 701–718.CrossRef
Rozsa, A., Günther, M., Rudd, E. M., & Boult, T. E. (2016). Are facial attributes adversarially robust? In Processing of international conference on pattern recognition (ICPR) (pp. 3121–3127). IEEE.
Rozsa, A., Günther, M., Rudd, E. M., & Boult, T. E. (2017). Facial attributes: Accuracy and adversarial robustness. Pattern Recognition Letters, 124, 100–108.CrossRef
Rudd, E. M., Günther, M., & Boult, T. E. (2016). Moon: A mixed objective optimization network for the recognition of facial attributes. In Proceedings of the European conference on computer vision (ECCV) (pp. 19–35). Springer.
Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). A semi-automatic methodology for facial landmark annotation. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW) (pp. 896–903).
Saito, M., Matsumoto, E., & Saito, S. (2017). Temporal generative adversarial nets with singular value clipping. In Proceedings of the IEEE international conference on computer vision (pp. 2830–2839).
Samangouei, P., Patel, V. M., & Chellappa, R. (2017). Facial attributes for active authentication on mobile devices. Image and Vision Computing, 58, 181–192.CrossRef
Sandeep, R. N., Verma, Y., & Jawahar, C. (2014). Relative parts: Distinctive parts for learning relative attributes. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3614–3621).
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 815–823).
Sethi, A., Singh, M., Singh, R., & Vatsa, M. (2018). Residual codean autoencoder for facial attribute analysis. Pattern Recognition Letters, 119, 157–165.CrossRef
Shen, W., & Liu, R. (2017). Learning residual images for face attribute manipulation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1225–1233). IEEE.
Shi, Z., Hospedales, T. M., & Xiang, T. (2015). Transferring a semantic representation for person re-identification and search. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 4184–4193). IEEE.
Singh, K. K., & Lee, Y. J. (2016). End-to-end localization and ranking for relative attributes. In Proceedings of the European conference on computer vision (ECCV) (pp. 753–769). Springer.
Smith, B. M., Zhang, L., Brandt, J., Lin, Z., & Yang, J. (2013). Exemplar-based face parsing. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3484–3491). IEEE.
Song, F., Tan, X., & Chen, S. (2014). Exploiting relationship between attributes for improved face verification. Computer Vision and Image Understanding, 122, 143–154.CrossRef
Song, L., Cao, J., Song, L., Hu, Y., & He, R. (2019). Geometry-aware face completion and editing. In Proceedings of the conference on artificial intelligence (AAAI).
Song, L., Lu, Z., He, R., Sun, Z., Tan, T. (2018a). Geometry guided adversarial facial expression synthesis. In Proceedings of the ACM international conference on multimedia (ACMMM) (pp. 627–635). ACM.
Song, L., Zhang, M., Wu, X., & He, R. (2018b). Adversarial discriminative heterogeneous face recognition. In Proceedings of the conference on artificial intelligence (AAAI).
Sun, R., Huang, C., Shi, J., & Ma, L. (2018c). Mask-aware photorealistic face attribute manipulation. arXiv preprint arXiv:​1804.​08882.
Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In Advances in neural information processing systems (NIPS) (pp. 1988–1996).
Suo, J., Zhu, S. C., Shan, S., & Chen, X. (2010). A compositional and dynamic model for face aging. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 32(3), 385–401.CrossRef
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the conference on artificial intelligence (AAAI) (Vol. 4, p. 12).
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. In Proceedings of the international conference on learning representations (ICLR).
Taherkhani, F., Nasrabadi, N. M., & Dawson, J. (2018). A deep face identification network enhanced by facial attributes prediction. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW) (pp. 553–560).
Toderici, G., O’malley, S.M., Passalis, G., Theoharis, T., Kakadiaris, I.A.: Ethnicity-and gender-based subject retrieval using 3-d face-recognition techniques. International Journal of Computer Vision (IJCV) 89(2-3), 382–391 (2010).
Trokielewicz, M., Czajka, A., & Maciejewicz, P. (2019). Iris recognition after death. IEEE Transactions on Information Forensics and Security, 14(6), 1501–1514.CrossRef
Tropp, J. A., Gilbert, A. C., & Strauss, M. J. (2006). Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit. Signal Processing, 86(3), 572–588.MATH
Wang, J., Cheng, Y., & Schmidt Feris, R. (2016). Walk and learn: Facial attribute representation learning from egocentric video and contextual data. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2295–2304).
Wang, Y., Wang, S., Qi, G., Tang, J., & Li, B. (2018). Weakly supervised facial attribute manipulation via deep adversarial network. In Proceedings of the IEEE winter conference on applications of computer vision (WACV) (pp. 112–121). IEEE.
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing (TIP), 13(4), 600–612.CrossRef
Wiles, O., Koepke, A., & Zisserman, A. (2018). Self-supervised learning of a facial attribute embedding from video. In Proceedings of the British machine vision conference (BMVC) (p. 302).
Wolf, L., Hassner, T., & Maoz, I. (2011). Face recognition in unconstrained videos with matched background similarity. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 529–534). IEEE Computer Society.
Wu, Y., & Ji, Q. (2017). Facial landmark detection: A literature survey. International Journal of Computer Vision (IJCV), 127, 115–142.CrossRef
Xiao, F., & Jae Lee, Y. (2015) Discovering the spatial extent of relative attributes. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 1458–1466).
Xiao, T., Hong, J., & Ma, J. (2017). DNA-GAN: Learning disentangled representations from multi-attribute images. In Proceedings of the international conference on learning representations workshop track (ICLRW).
Xiao, T., Hong, J., & Ma, J. (2018). Elegant: Exchanging latent encodings with gan for transferring multiple face attributes. In Proceedings of the European conference on computer vision (ECCV) (pp. 168–184).
Yan, X., Yang, J., Sohn, K., & Lee, H. (2016). Attribute2image: Conditional image generation from visual attributes. In Proceedings of the European conference on computer vision (ECCV) (pp. 776–791). Springer.
Yue, Y., Finley, T., Radlinski, F., & Joachims, T. (2007). A support vector method for optimizing average precision. In Proceedings of the annual international ACM SIGIR conference on research and development in information retrieval (pp. 271–278). ACM.
Zhang, G., Kan, M., Shan, S., & Chen, X. (2018a). Generative adversarial network with spatial attention for face attribute editing. In Proceedings of the European conference on computer vision (ECCV) (pp. 417–432).
Zhang, J., Shu, Y., Xu, S., Cao, G., Zhong, F., Liu, M., & Qin, X. (2018b). Sparsely grouped multi-task generative adversarial networks for facial attribute manipulation. In ACM Multimedia conference on multimedia conference (ACMMM) (pp. 392–401). ACM.
Zhang, J., Zhong, F., Cao, G., & Qin, X. (2017a). ST-GAN: Unsupervised facial image semantic transformation using generative adversarial networks. In Proceedings of the Asian conference on machine learning (ACML) (pp. 248–263).
Zhang, N., Paluri, M., Ranzato, M., Darrell, T., & Bourdev, L. (2014). Panda: Pose aligned networks for deep attribute modeling. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1637–1644). IEEE.
Zhang, S., He, R., Sun, Z., & Tan, T. (2018c). Demeshnet: Blind face inpainting for deep meshface verification. IEEE Transactions on Information Forensics and Security (TIFS), 13(3), 637–647.CrossRef
Zhang, Z., Song, Y., & Qi, H. (2017b). Age progression/regression by conditional adversarial autoencoder. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (Vol. 2, pp. 4352–4360).
Zhong, Y., Sullivan, J., & Li, H. (2016a). Face attribute prediction using off-the-shelf CNN features. In Proceedings of the IEEE international conference on biometrics (ICB) (pp. 1–7). IEEE.
Zhong, Y., Sullivan, J., & Li, H. (2016b). Leveraging mid-level deep representations for predicting face attributes in the wild. In Proceedings of the IEEE international conference on image processing (ICIP) (pp. 3239–3243). IEEE.
Zhou, S., Xiao, T., Yang, Y., Feng, D., He, Q., & He, W. (2017). Genegan: Learning object transfiguration and attribute subspace from unpaired data. In Proceedings of the British machine vision conference (BMVC).
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 2242–2251).
Zhuang, N., Yan, Y., Chen, S., & Wang, H. (2018). Multi-task learning of cascaded CNN for facial attribute classification. In Proceedings of the international conference on pattern recognition (ICPR) (pp. 2069–2074). IEEE.
Zhuang, N., Yan, Y., Chen, S., Wang, H., & Shen, C. (2018). Multi-label learning based deep transfer neural network for facial attribute classification. Pattern Recognition, 80, 225–240.CrossRef
Metadaten
Titel
A Survey of Deep Facial Attribute Analysis
verfasst von
Xin Zheng
Yanqing Guo
Huaibo Huang
Yi Li
Ran He
Publikationsdatum
24.03.2020
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 8-9/2020
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-020-01308-z

Weitere Artikel der Ausgabe 8-9/2020

International Journal of Computer Vision 8-9/2020 Zur Ausgabe

Guest Editorial

Efficient Visual Recognition

OriginalPaper

Fine-Grained Multi-human Parsing

OriginalPaper

Learning an Evolutionary Embedding via Massive Knowledge Distillation

OriginalPaper

SSN: Learning Sparse Switchable Normalization via SparsestMax

OriginalPaper

Anchor-Based Self-Ensembling for Semi-Supervised Deep Pairwise Hashing

OriginalPaper

Hadamard Matrix Guided Online Hashing