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Artykuły w czasopismach na temat "Unconstrained face recognition"
Deng, Weihong, Jiani Hu, Zhongjun Wu i Jun Guo. "Lighting-aware face frontalization for unconstrained face recognition". Pattern Recognition 68 (sierpień 2017): 260–71. http://dx.doi.org/10.1016/j.patcog.2017.03.024.
Pełny tekst źródłaMasi, Iacopo, Anh Tuấn Trần, Tal Hassner, Gozde Sahin i Gérard Medioni. "Face-Specific Data Augmentation for Unconstrained Face Recognition". International Journal of Computer Vision 127, nr 6-7 (1.04.2019): 642–67. http://dx.doi.org/10.1007/s11263-019-01178-0.
Pełny tekst źródłaTyagi, Ranbeer, Geetam Singh Tomar i Laxmi Shrivastava. "Unconstrained Face Recognition Quality: A Review". International Journal of Signal Processing, Image Processing and Pattern Recognition 9, nr 11 (30.11.2016): 199–210. http://dx.doi.org/10.14257/ijsip.2016.9.11.18.
Pełny tekst źródłaVinay, A., Abhijay Gupta, Aprameya Bharadwaj, Arvind Srinivasan, K. N. Balasubramanya Murthy i S. Natarajan. "Unconstrained Face Recognition using Bayesian Classification". Procedia Computer Science 143 (2018): 519–27. http://dx.doi.org/10.1016/j.procs.2018.10.425.
Pełny tekst źródłaRifaee, Mustafa, Mohammad Al Rawajbeh, Basem AlOkosh i Farhan AbdelFattah. "A New approach to Recognize Human Face Under Unconstrained Environment". International Journal of Advances in Soft Computing and its Applications 14, nr 2 (20.07.2022): 2–13. http://dx.doi.org/10.15849/ijasca.220720.01.
Pełny tekst źródłaYu, Aihua, Gang Li, Beiping Hou, Hongan Wang i Gaoya Zhou. "A novel framework for face recognition using robust local representation–based classification". International Journal of Distributed Sensor Networks 15, nr 3 (marzec 2019): 155014771983608. http://dx.doi.org/10.1177/1550147719836082.
Pełny tekst źródłaTORBATI, Ali, i Önsen TOYGAR. "MASKED AND UNMASKED FACE RECOGNITION ON UNCONSTRAINED FACIAL IMAGES USING HAND-CRAFTED METHODS". Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 26, Özel Sayı (12.12.2023): 1133–39. http://dx.doi.org/10.17780/ksujes.1339868.
Pełny tekst źródłaRuan, Shuai, Chaowei Tang, Xu Zhou, Zhuoyi Jin, Shiyu Chen, Haotian Wen, Hongbin Liu i Dong Tang. "Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene". Applied Sciences 10, nr 13 (7.07.2020): 4669. http://dx.doi.org/10.3390/app10134669.
Pełny tekst źródłaTong, Ying, Jiachao Zhang i Rui Chen. "Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition". Electronics 8, nr 5 (7.05.2019): 503. http://dx.doi.org/10.3390/electronics8050503.
Pełny tekst źródłaAgrawal, Amrit Kumar, i Yogendra Narain Singh. "Unconstrained face recognition using deep convolution neural network". International Journal of Information and Computer Security 12, nr 2/3 (2020): 332. http://dx.doi.org/10.1504/ijics.2020.10026788.
Pełny tekst źródłaRozprawy doktorskie na temat "Unconstrained face recognition"
Zhou, Shaohua. "Unconstrained face recognition". College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1800.
Pełny tekst źródłaThesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Wei, Xingjie. "Unconstrained face recognition with occlusions". Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/66778/.
Pełny tekst źródłaJuefei-Xu, Felix. "Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond". Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1189.
Pełny tekst źródłaLopes, Daniel Pedro Ferreira. "Face verication for an access control system in unconstrained environment". Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23395.
Pełny tekst źródłaO reconhecimento facial tem vindo a receber bastante atenção ao longo dos últimos anos não só na comunidade cientifica, como também no ramo comercial. Uma das suas várias aplicações e o seu uso num controlo de acessos onde um indivíduo tem uma ou várias fotos associadas a um documento de identificação (também conhecido como verificação de identidade). Embora atualmente o estado da arte apresente muitos estudos em que tanto apresentam novos algoritmos de reconhecimento como melhorias aos já desenvolvidos, existem mesmo assim muitos problemas ligados a ambientes não controlados, a aquisição de imagem e a escolha dos algoritmos de deteção e de reconhecimento mais eficazes. Esta tese aborda um ambiente desafiador para a verificação facial: um cenário não controlado para o acesso a infraestruturas desportivas. Uma vez que não existem condições de iluminação controladas nem plano de fundo controlado, isto torna um cenário complicado para a implementação de um sistema de verificação facial. Esta tese apresenta um estudo sobre os mais importantes algoritmos de detecção e reconhecimento facial assim como técnicas de pré-processamento tais como o alinhamento facial, a igualização de histograma, com o objetivo de melhorar a performance dos mesmos. Também em são apresentados dois métodos para a aquisição de imagens envolvendo a seleção de imagens e calibração da câmara. São apresentados resultados experimentais detalhados baseados em duas bases de dados criadas especificamente para este estudo. No uso de técnicas de pré-processamento apresentadas, foi possível presenciar melhorias até 20% do desempenho dos algoritmos de reconhecimento referentes a verificação de identidade. Com os métodos apresentados para os testes ao ar livre, foram conseguidas melhorias na ordem dos 30%.
Face Recognition has been received great attention over the last years, not only on the research community, but also on the commercial side. One of the many uses of face recognition is its use on access control systems where a person has one or several photos associated to an Identi cation Document (also known as identity veri cation). Although there are many studies nowadays, both presenting new algorithms or just improvements of the already developed ones, there are still many open problems regarding face recognition in uncontrolled environments, from the image acquisition conditions to the choice of the most e ective detection and recognition algorithms, just to name a few. This thesis addresses a challenging environment for face veri cation: an unconstrained environment for sports infrastructures access. As there are no controlled lightning conditions nor controlled background, this makes a di cult scenario to implement a face veri cation system. This thesis presents a study of some of the most important facial detection and recognition algorithms as well as some pre-processing techniques, such as face alignment and histogram equalization, with the aim to improve their performance. It also introduces some methods for a more e cient image acquisition based on image selection and camera calibration, specially designed for addressing this problem. Detailed experimental results are presented based on two new databases created speci cally for this study. Using pre-processing techniques, it was possible to improve the recognition algorithms performances up to 20% regarding veri cation results. With the methods presented for the outdoor tests, performances had improvements up to 30%
Stone, Zachary. "Face Identification in the Internet Era". Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10397.
Pełny tekst źródłaEngineering and Applied Sciences
Vu, Ngoc-Son. "Contributions à la reconnaissance de visages à partir d'une seule image et dans un contexte non-contrôlé". Phd thesis, Grenoble INPG, 2010. https://theses.hal.science/tel-00574547v1.
Pełny tekst źródłaAlthough having been an active research topic for 30 years, recognizing a person from surveillance having seen only one image is unsolved. Within this context, the two greatest challenges are the variations of pose and illumination. Moreover, there are strict constraints upon the complexity in both terms of computational time and stockage requirements. The work developed throughout this dissertation gives several advantages in the context of real-time and unconstrained face recognition. Firstly, an illumination normalization method simulating the performance of human retina is proposed as preprocessing algorithm. Secondly, we propose novel features called POEM (Patterns of Oriented Edge Magnitudes) for representing a local image structure. This descriptor is discriminative and robust to exterior variations (variations of pose, illumination, expression and pose that we always see when dealing with face images). Thirdly, a statistical model for robust face recognition across poses, entered on modeling how facial patch appearance changes as the viewpoint varies, is proposed. Finally, a novel approach modeling the spatial relationships between face components is developed. Except the last algorithm, all proposed methods are very fast and are therefore suitable for the constraints upon real-time of surveillance applications
Mudunuri, Sivaram Prasad. "Face Recognition in Unconstrained Environment". Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5113.
Pełny tekst źródłaReigoto, Anabela Machado. "Face Detection and Recognition in Unconstrained Scenarios". Master's thesis, 2019. https://hdl.handle.net/10216/122842.
Pełny tekst źródłaSanyal, Soubhik. "Discriminative Descriptors for Unconstrained Face and Object Recognition". Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4177.
Pełny tekst źródłaFreitas, Tiago Daniel Santos. "3D Face Recognition Under Unconstrained settings using Low-Cost Sensors". Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/84513.
Pełny tekst źródłaKsiążki na temat "Unconstrained face recognition"
Unconstrained Face Recognition. Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-29486-5.
Pełny tekst źródłaChellappa, Rama, Shaohua Kevin Zhou i Wenyi Zhao. Unconstrained Face Recognition. Springer, 2010.
Znajdź pełny tekst źródłaUnconstrained Face Recognition (International Series on Biometrics). Springer, 2005.
Znajdź pełny tekst źródłaChellappa, Rama, Shaohua Kevin Zhou i Wenyi Zhao. Unconstrained Face Recognition (International Series on Biometrics Book 5). Springer, 2006.
Znajdź pełny tekst źródłaCzęści książek na temat "Unconstrained face recognition"
Ouanan, Hamid, Mohammed Ouanan i Brahim Aksasse. "Myface: Unconstrained Face Recognition". W Lecture Notes in Networks and Systems, 86–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69137-4_9.
Pełny tekst źródłaKocjan, Przemysław, i Khalid Saeed. "Face Recognition in Unconstrained Environment". W Biometrics and Kansei Engineering, 21–42. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5608-7_2.
Pełny tekst źródłaGong, Xun, Jun Luo i Zehua Fu. "Normalization for Unconstrained Pose-Invariant 3D Face Recognition". W Biometric Recognition, 1–8. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_1.
Pełny tekst źródłaLiu, Feng, Minchul Kim, Anil Jain i Xiaoming Liu. "Controllable and Guided Face Synthesis for Unconstrained Face Recognition". W Lecture Notes in Computer Science, 701–19. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_41.
Pełny tekst źródłaWu, Zhongjun, Weihong Deng i Zhanfu An. "Illumination-Recovered Pose Normalization for Unconstrained Face Recognition". W Computer Vision – ACCV 2016, 217–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54187-7_15.
Pełny tekst źródłaTome, Pedro, Ruben Vera-Rodriguez, Julian Fierrez i Javier Ortega-García. "Variability Compensation Using NAP for Unconstrained Face Recognition". W Advances in Intelligent and Soft Computing, 129–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28765-7_17.
Pełny tekst źródłaMoghekar, Rajeshwar, i Sachin Ahuja. "Face Recognition in Unconstrained Environment Using Deep Learning". W Soft Computing for Intelligent Systems, 241–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1048-6_18.
Pełny tekst źródłaMartin, Michael, i Thirimachos Bourlai. "Unconstrained Face Recognition Using Cell Phone Devices: Faces in the Wild". W Advanced Sciences and Technologies for Security Applications, 129–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39489-9_7.
Pełny tekst źródłaYan, Yan, Hanzi Wang, Cuihua Li, Chenhui Yang i Bineng Zhong. "A Novel Unconstrained Correlation Filter and Its Application in Face Recognition". W Intelligent Science and Intelligent Data Engineering, 32–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_5.
Pełny tekst źródłaRuiz-del-Solar, Javier, Rodrigo Verschae, Gabriel Hermosilla i Mauricio Correa. "Thermal Face Recognition in Unconstrained Environments Using Histograms of LBP Features". W Local Binary Patterns: New Variants and Applications, 219–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39289-4_10.
Pełny tekst źródłaStreszczenia konferencji na temat "Unconstrained face recognition"
Huang, Gary B., Manjunath Narayana i Erik Learned-Miller. "Towards unconstrained face recognition". W 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2008. http://dx.doi.org/10.1109/cvprw.2008.4562973.
Pełny tekst źródła"Subtasks of Unconstrained Face Recognition". W International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004694201130121.
Pełny tekst źródłaSrisawasd, Wipawee, i Sartra Wongthanavasu. "Face Recognition In Unconstrained Environment". W 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2018. http://dx.doi.org/10.1109/jcsse.2018.8457336.
Pełny tekst źródłaSaffar, Mohammad Taghi, Banafsheh Rekabdar, Sushil Louis i Mircea Nicolescu. "Face recognition in unconstrained environments". W 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280803.
Pełny tekst źródłaDong-Ju Kim, Sang-Heon Lee, Myoung-Kyu Sohn, Byungmin Kim i Hyunduk Kim. "Face recognition in unconstrained environments". W 2013 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2013. http://dx.doi.org/10.1109/icce.2013.6486832.
Pełny tekst źródłaShao, Xiaohu, Junliang Xing, Jiangjing Lv, Chunlin Xiao, Pengcheng Liu, Youji Feng i Cheng Cheng. "Unconstrained Face Alignment Without Face Detection". W 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.258.
Pełny tekst źródłaRoth, Joseph, Yiying Tong i Xiaoming Liu. "Unconstrained 3D face reconstruction". W 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298876.
Pełny tekst źródłaDao, Viet-Anh, Dang-Ha Nguyen, Viet-Bac Nguyen, Thom Tran Thi i Hoang-Anh Nguyen The. "Face Recognition System for Unconstrained Condition". W 2023 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2023. http://dx.doi.org/10.1109/atc58710.2023.10318921.
Pełny tekst źródłaTyagi, Ranbeer, Geetam Singh Tomar i Laxmi Shrivastava. "Unconstrained Face Recognition from Image Sequence". W 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2023. http://dx.doi.org/10.1109/aic57670.2023.10263884.
Pełny tekst źródłaGunther, M., P. Hu, C. Herrmann, C. H. Chan, M. Jiang, S. Yang, A. R. Dhamija i in. "Unconstrained Face Detection and Open-Set Face Recognition Challenge". W 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2017. http://dx.doi.org/10.1109/btas.2017.8272759.
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