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Статті в журналах з теми "Unconstrained face recognition"
Deng, Weihong, Jiani Hu, Zhongjun Wu, and Jun Guo. "Lighting-aware face frontalization for unconstrained face recognition." Pattern Recognition 68 (August 2017): 260–71. http://dx.doi.org/10.1016/j.patcog.2017.03.024.
Повний текст джерелаMasi, Iacopo, Anh Tuấn Trần, Tal Hassner, Gozde Sahin, and Gérard Medioni. "Face-Specific Data Augmentation for Unconstrained Face Recognition." International Journal of Computer Vision 127, no. 6-7 (April 1, 2019): 642–67. http://dx.doi.org/10.1007/s11263-019-01178-0.
Повний текст джерелаTyagi, Ranbeer, Geetam Singh Tomar, and Laxmi Shrivastava. "Unconstrained Face Recognition Quality: A Review." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 11 (November 30, 2016): 199–210. http://dx.doi.org/10.14257/ijsip.2016.9.11.18.
Повний текст джерелаVinay, A., Abhijay Gupta, Aprameya Bharadwaj, Arvind Srinivasan, K. N. Balasubramanya Murthy, and 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.
Повний текст джерелаRifaee, Mustafa, Mohammad Al Rawajbeh, Basem AlOkosh, and Farhan AbdelFattah. "A New approach to Recognize Human Face Under Unconstrained Environment." International Journal of Advances in Soft Computing and its Applications 14, no. 2 (July 20, 2022): 2–13. http://dx.doi.org/10.15849/ijasca.220720.01.
Повний текст джерелаYu, Aihua, Gang Li, Beiping Hou, Hongan Wang, and Gaoya Zhou. "A novel framework for face recognition using robust local representation–based classification." International Journal of Distributed Sensor Networks 15, no. 3 (March 2019): 155014771983608. http://dx.doi.org/10.1177/1550147719836082.
Повний текст джерелаTORBATI, Ali, and 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 (December 12, 2023): 1133–39. http://dx.doi.org/10.17780/ksujes.1339868.
Повний текст джерелаRuan, Shuai, Chaowei Tang, Xu Zhou, Zhuoyi Jin, Shiyu Chen, Haotian Wen, Hongbin Liu, and Dong Tang. "Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene." Applied Sciences 10, no. 13 (July 7, 2020): 4669. http://dx.doi.org/10.3390/app10134669.
Повний текст джерелаTong, Ying, Jiachao Zhang, and Rui Chen. "Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition." Electronics 8, no. 5 (May 7, 2019): 503. http://dx.doi.org/10.3390/electronics8050503.
Повний текст джерелаAgrawal, Amrit Kumar, and Yogendra Narain Singh. "Unconstrained face recognition using deep convolution neural network." International Journal of Information and Computer Security 12, no. 2/3 (2020): 332. http://dx.doi.org/10.1504/ijics.2020.10026788.
Повний текст джерелаДисертації з теми "Unconstrained face recognition"
Zhou, Shaohua. "Unconstrained face recognition." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1800.
Повний текст джерелаThesis 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/.
Повний текст джерелаJuefei-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.
Повний текст джерелаLopes, 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.
Повний текст джерелаO 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.
Повний текст джерелаEngineering 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.
Повний текст джерелаAlthough 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.
Повний текст джерелаReigoto, Anabela Machado. "Face Detection and Recognition in Unconstrained Scenarios." Master's thesis, 2019. https://hdl.handle.net/10216/122842.
Повний текст джерелаSanyal, Soubhik. "Discriminative Descriptors for Unconstrained Face and Object Recognition." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4177.
Повний текст джерелаFreitas, 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.
Повний текст джерелаКниги з теми "Unconstrained face recognition"
Unconstrained Face Recognition. Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-29486-5.
Повний текст джерелаChellappa, Rama, Shaohua Kevin Zhou, and Wenyi Zhao. Unconstrained Face Recognition. Springer, 2010.
Знайти повний текст джерелаUnconstrained Face Recognition (International Series on Biometrics). Springer, 2005.
Знайти повний текст джерелаChellappa, Rama, Shaohua Kevin Zhou, and Wenyi Zhao. Unconstrained Face Recognition (International Series on Biometrics Book 5). Springer, 2006.
Знайти повний текст джерелаЧастини книг з теми "Unconstrained face recognition"
Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Myface: Unconstrained Face Recognition." In 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.
Повний текст джерелаKocjan, Przemysław, and Khalid Saeed. "Face Recognition in Unconstrained Environment." In 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.
Повний текст джерелаGong, Xun, Jun Luo, and Zehua Fu. "Normalization for Unconstrained Pose-Invariant 3D Face Recognition." In Biometric Recognition, 1–8. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_1.
Повний текст джерелаLiu, Feng, Minchul Kim, Anil Jain, and Xiaoming Liu. "Controllable and Guided Face Synthesis for Unconstrained Face Recognition." In Lecture Notes in Computer Science, 701–19. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_41.
Повний текст джерелаWu, Zhongjun, Weihong Deng, and Zhanfu An. "Illumination-Recovered Pose Normalization for Unconstrained Face Recognition." In Computer Vision – ACCV 2016, 217–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54187-7_15.
Повний текст джерелаTome, Pedro, Ruben Vera-Rodriguez, Julian Fierrez, and Javier Ortega-García. "Variability Compensation Using NAP for Unconstrained Face Recognition." In 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.
Повний текст джерелаMoghekar, Rajeshwar, and Sachin Ahuja. "Face Recognition in Unconstrained Environment Using Deep Learning." In Soft Computing for Intelligent Systems, 241–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1048-6_18.
Повний текст джерелаMartin, Michael, and Thirimachos Bourlai. "Unconstrained Face Recognition Using Cell Phone Devices: Faces in the Wild." In 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.
Повний текст джерелаYan, Yan, Hanzi Wang, Cuihua Li, Chenhui Yang, and Bineng Zhong. "A Novel Unconstrained Correlation Filter and Its Application in Face Recognition." In 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.
Повний текст джерелаRuiz-del-Solar, Javier, Rodrigo Verschae, Gabriel Hermosilla, and Mauricio Correa. "Thermal Face Recognition in Unconstrained Environments Using Histograms of LBP Features." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Unconstrained face recognition"
Huang, Gary B., Manjunath Narayana, and Erik Learned-Miller. "Towards unconstrained face recognition." In 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.
Повний текст джерела"Subtasks of Unconstrained Face Recognition." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004694201130121.
Повний текст джерелаSrisawasd, Wipawee, and Sartra Wongthanavasu. "Face Recognition In Unconstrained Environment." In 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2018. http://dx.doi.org/10.1109/jcsse.2018.8457336.
Повний текст джерелаSaffar, Mohammad Taghi, Banafsheh Rekabdar, Sushil Louis, and Mircea Nicolescu. "Face recognition in unconstrained environments." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280803.
Повний текст джерелаDong-Ju Kim, Sang-Heon Lee, Myoung-Kyu Sohn, Byungmin Kim, and Hyunduk Kim. "Face recognition in unconstrained environments." In 2013 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2013. http://dx.doi.org/10.1109/icce.2013.6486832.
Повний текст джерелаShao, Xiaohu, Junliang Xing, Jiangjing Lv, Chunlin Xiao, Pengcheng Liu, Youji Feng, and Cheng Cheng. "Unconstrained Face Alignment Without Face Detection." In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.258.
Повний текст джерелаRoth, Joseph, Yiying Tong, and Xiaoming Liu. "Unconstrained 3D face reconstruction." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298876.
Повний текст джерелаDao, Viet-Anh, Dang-Ha Nguyen, Viet-Bac Nguyen, Thom Tran Thi, and Hoang-Anh Nguyen The. "Face Recognition System for Unconstrained Condition." In 2023 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2023. http://dx.doi.org/10.1109/atc58710.2023.10318921.
Повний текст джерелаTyagi, Ranbeer, Geetam Singh Tomar, and Laxmi Shrivastava. "Unconstrained Face Recognition from Image Sequence." In 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2023. http://dx.doi.org/10.1109/aic57670.2023.10263884.
Повний текст джерелаGunther, M., P. Hu, C. Herrmann, C. H. Chan, M. Jiang, S. Yang, A. R. Dhamija, et al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge." In 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2017. http://dx.doi.org/10.1109/btas.2017.8272759.
Повний текст джерела