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Статті в журналах з теми "Low Resolution Face Recognition"
Chen, Jin, Jun Chen, Zheng Wang, Chao Liang, and Chia-Wen Lin. "Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition." IEEE Signal Processing Letters 27 (2020): 645–49. http://dx.doi.org/10.1109/lsp.2020.2986942.
Повний текст джерелаWang, Zhifei, Zhenjiang Miao, Q. M. Jonathan Wu, Yanli Wan, and Zhen Tang. "Low-resolution face recognition: a review." Visual Computer 30, no. 4 (August 6, 2013): 359–86. http://dx.doi.org/10.1007/s00371-013-0861-x.
Повний текст джерелаZou, W. W. W., and P. C. Yuen. "Very Low Resolution Face Recognition Problem." IEEE Transactions on Image Processing 21, no. 1 (January 2012): 327–40. http://dx.doi.org/10.1109/tip.2011.2162423.
Повний текст джерелаHong, Sungeun, and Jongbin Ryu. "Unsupervised Face Domain Transfer for Low-Resolution Face Recognition." IEEE Signal Processing Letters 27 (2020): 156–60. http://dx.doi.org/10.1109/lsp.2019.2963001.
Повний текст джерелаXu, Xiang, Wanquan Liu, and Ling Li. "Low Resolution Face Recognition in Surveillance Systems." Journal of Computer and Communications 02, no. 02 (2014): 70–77. http://dx.doi.org/10.4236/jcc.2014.22013.
Повний текст джерелаMostafa, Eslam, Riad Hammoud, Asem Ali, and Aly Farag. "Face recognition in low resolution thermal images." Computer Vision and Image Understanding 117, no. 12 (December 2013): 1689–94. http://dx.doi.org/10.1016/j.cviu.2013.07.010.
Повний текст джерелаHan, Feng, Xudong Wang, Furao Shen, and Jian Zhao. "C-Face: Using Compare Face on Face Hallucination for Low-Resolution Face Recognition." Journal of Artificial Intelligence Research 74 (August 16, 2022): 1715–37. http://dx.doi.org/10.1613/jair.1.13816.
Повний текст джерелаLi, Sisi, Zhonghua Liu, Di Wu, Hua Huo, Haijun Wang, and Kaibing Zhang. "Low-resolution face recognition based on feature-mapping face hallucination." Computers and Electrical Engineering 101 (July 2022): 108136. http://dx.doi.org/10.1016/j.compeleceng.2022.108136.
Повний текст джерелаPeng, Yuxi, Luuk Spreeuwers, and Raymond Veldhuis. "Low‐resolution face alignment and recognition using mixed‐resolution classifiers." IET Biometrics 6, no. 6 (April 24, 2017): 418–28. http://dx.doi.org/10.1049/iet-bmt.2016.0026.
Повний текст джерелаMaity, Sayan, Mohamed Abdel-Mottaleb, and Shihab S. Asfour. "Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video." Electronics 10, no. 9 (April 24, 2021): 1013. http://dx.doi.org/10.3390/electronics10091013.
Повний текст джерелаДисертації з теми "Low Resolution Face Recognition"
Arachchige, Somi Ruwan Budhagoda. "Face recognition in low resolution video sequences using super resolution /." Online version of thesis, 2008. http://hdl.handle.net/1850/7770.
Повний текст джерелаRoeder, James Roger. "Assessment of super-resolution for face recognition from very-low resolution images." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
Повний текст джерелаKramer, Annika. "Model based methods for locating, enhancing and recognising low resolution objects in video." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/585.
Повний текст джерелаSILVA, José Ivson Soares da. "Reconhecimento facial em imagens de baixa resolução." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16367.
Повний текст джерелаMade available in DSpace on 2016-04-07T12:14:52Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dissertação_jiss_ciênciadacomputação.pdf: 2819671 bytes, checksum: 98f583c2b7105c3a5b369b2b48097633 (MD5) Previous issue date: 2015-02-24
FADE
Tem crescido o uso de sistemas computacionais para reconhecimento de pessoas por meio de dados biométricos, consequentemente os métodos para realizar o reconhecimento tem evoluído. A biometria usada no reconhecimento pode ser face, voz, impressão digital ou qualquer característica física capaz de distinguir as pessoas. Mudanças causadas por cirurgias, envelhecimento ou cicatrizes, podem não causar mudanças significativas nas características faciais tornando possível o reconhecimento após essas mudanças de aparência propositais ou não. Por outro lado tais mudanças se tornam um desafio para sistemas de reconhecimento automático. Além das mudanças físicas há outros fatores na obtenção da imagem que influenciam o reconhecimento facial como resolução da imagem, posição da face em relação a câmera, iluminação do ambiente, oclusão, expressão. A distância que uma pessoa aparece na cena modifica a resolução da região da sua face, o objetivo de sistemas direcionados a esse contexto é que a influência da resolução nas taxas de reconhecimento seja minimizada. Uma pessoa mais distante da câmera tem sua face na imagem numa resolução menor que uma que esteja mais próxima. Sistemas de reconhecimento facial têm um menor desempenho ao tratar imagens faciais de baixa resolução. Uma das fases de um sistema de reconhecimento é a extração de características, que processa os dados de entrada e fornece um conjunto de informações mais representativas das imagens. Na fase de extração de características os padrões da base de dados de treinamento são recebidos numa mesma dimensão, ou seja, no caso de imagens numa mesma resolução. Caso as imagens disponíveis para o treinamento sejam de resoluções diferentes ou as imagens de teste sejam de resolução diferente do treinamento, faz-se necessário que na fase de pré-processamento haja um tratamento de resolução. O tratamento na resolução pode ser aplicando um aumento da resolução das imagens menores ou redução da resolução das imagens maiores. O aumento da resolução não garante um ganho de informação que possa melhorar o desempenho dos sistemas. Neste trabalho são desenvolvidos dois métodos executados na fase de extração de características realizada por Eigenface, os vetores de características são redimensionados para uma nova escala menor por meio de interpolação, semelhante ao que acontece no redimensionamento de imagens. No primeiro método, após a extração de características, os vetores de características e as imagens de treinamento são redimensionados. Então, as imagens de treinamento e teste são projetadas no espaço de características pelos vetores de dimensão reduzida. No segundo método, apenas os vetores de características são redimensionados e multiplicados por um fator de compensação. Então, as imagens de treinamento são projetadas pelos vetores originais e as imagens de teste são projetadas pelos vetores reduzidos para o mesmo espaço. Os métodos propostos foram testados em 4 bases de dados de reconhecimento facial com a presença de problemas de variação de iluminação, variação de expressão facial, presença óculos e posicionamento do rosto.
In the last decades the use of computational systems to recognize people by biometric data is increasing, consequently the efficacy of methods to perform recognition is improving. The biometry used for recognition can be face, voice, fingerprint or other physical feature that enables the distiction of different persons. Facial changes caused by surgery, aging or scars, does not necessarily causes significant changes in facial features. For a human it is possible recognize other person after these interventions of the appearance. On the other hand, these interventions become a challenge to computer recognition systems. Beyond the physical changes there are other factors in aquisition of an image that influence the face recognition such as the image resolution, position between face and camera, light from environment, occlusions and variation of facial expression. The distance that a person is at image aquisition changes the resolution of face image. The objective of systems for this context is to minimize the influence of the image resolution for the recognition. A person more distant from the camera has the image of the face in a smaller resolution than a person near the camera. Face recognition systems have a poor performance to analyse low resolution image. One of steps of a recognition system is the features extraction that processes the input data so provides more representative images. In the features extraction step the images from the training database are received at same dimension, in other words, to analyse the images they have the same resolution. If the training images have different resolutions of test images it is necessary a preprocessing to normalize the image resolution. The preprocessing of an image can be to increase the resolution of small images or to reduce the resolution of big images. The increase resolution does not guarantee that there is a information gain that can improves the performance of the recognition systems. In this work two methods are developed at features extraction step based on Eigenface. The feature vectors are resized to a smaller scale, similar to image resize. In first method, after the feature extraction step, the feature vectors and the training images are resized. Then the training and test images are projected to feature space by the resized feature vectors. In second method, only the feature vectors are resized and multiplied by a compensation factor. The training images are projected by original feature vectors and the test images are projected by resized feature vectors to the same space. The proposed methods were tested in 4 databases of face recognition with presence of light variation, variation of facial expression, use of glasses and face position.
Prado, Kelvin Salton do. "Comparação de técnicas de reconhecimento facial para identificação de presença em um ambiente real e semicontrolado." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-07012018-222531/.
Повний текст джерелаFace recognition is a task that human beings perform naturally in their everyday lives, usually with no effort at all. To machines, however, this process is not so simple. With the increasing computational power of current machines, a great interest was created in the field of digital videos and images processing, with applications in most diverse areas of knowledge. This work aims to compare face recognition techniques already know in the literature, in order to identify which technique has the best performance in a real and semicontrolled environment. As a secondary objective, we evaluate the possibility of using one or more face recognition techniques to automatically identify the presence of students in a martial arts classroom using images from the surveillance cameras installed in the room, taking into account important aspects such as images with low sharpness, illumination variation, constant movement of students and the fact that the cameras are at a fixed angle. This work is related to the Image Processing and Pattern Recognition areas, and integrates the research line \"Presence Monitoring\" of the project entitled \"Education and Monitoring of Physical Activities using Artificial Intelligence Techniques\" (Process 2014.1.923.86.4, published in DOE 125 (45) on 03/10/2015), developed as a partnership between the University of São Paulo, Campo Limpo Paulista Faculty, and Kungfu-Wushu Central Academy. With the experiments performed and presented in this work it was possible to conclude that, amongst all face recognition methods that were tested, Local Binary Patterns had the best performance in the proposed environment. On the other hand, Eigenfaces had the worse performance according to the experiments. Moreover, it was also possible to conclude that it is not feasible to perform the automatic presence detection reliably in the proposed environment, since the face recognition rate was relatively low, compared to the state of the art which uses, in general, more friendly test environments but at the same time less likely found in our daily lives. We believe that it was possible to achieve the objectives proposed by this work and that can contribute to the current state of the art in the computer vision field and, more precisely, in the face recognition area. Finally, some future work is suggested that can be used as a starting point for the continuation of this work or even for new researches related to this topic
Bilson, Amy Jo. "Image size and resolution in face recognition /." Thesis, Connect to this title online; UW restricted, 1987. http://hdl.handle.net/1773/9166.
Повний текст джерелаLin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/16703/1/Frank_Lin_Thesis.pdf.
Повний текст джерелаLin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16703/.
Повний текст джерелаNaim, Mamoun. "New techniques in the recognition of very low resolution images." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266343.
Повний текст джерелаLi, Kai Chee. "Object identification from a low resolution laser radar system." Thesis, University of Surrey, 1992. http://epubs.surrey.ac.uk/844536/.
Повний текст джерелаКниги з теми "Low Resolution Face Recognition"
Keitel, Stefan. Development of a microprocessor based low cost, low resolution image recognition system. Uxbridge: Brunel University, 1987.
Знайти повний текст джерелаVideo-to-Video Face Recognition for Low-Quality Surveillance Data. KIT Scientific Publishing, 2018.
Знайти повний текст джерелаAntonios, Tzanakopoulos. Part I The International Law of Tainted Money, 5 International Legal Sources IV—the European Union and the Council of Europe. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198716587.003.0005.
Повний текст джерелаAlex, Mills. Part X Judicial Review, Judicial Performance, and Enforcement, 31 The Principled English Ambivalence to Law and Dispute Resolution Beyond the State. Oxford University Press, 2016. http://dx.doi.org/10.1093/law/9780198783206.003.0032.
Повний текст джерелаBuhlmann, Ulrike, and Andrea S. Hartmann. Cognitive and Emotional Processing in Body Dysmorphic Disorder. Edited by Katharine A. Phillips. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190254131.003.0022.
Повний текст джерелаMartin, Graham R. The Sensory Ecology of Collisions and Entrapment. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199694532.003.0009.
Повний текст джерелаSiklos, Pierre L. Central Banks into the Breach. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190228835.001.0001.
Повний текст джерелаTir, Jaroslav, and Johannes Karreth. Incentivizing Peace. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190699512.001.0001.
Повний текст джерелаCukierman, Alex. Central Banks. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.64.
Повний текст джерелаЧастини книг з теми "Low Resolution Face Recognition"
Cheng, Zhiyi, Xiatian Zhu, and Shaogang Gong. "Low-Resolution Face Recognition." In Computer Vision – ACCV 2018, 605–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20893-6_38.
Повний текст джерелаMarciniak, Tomasz, Adam Dabrowski, Agata Chmielewska, and Radosław Weychan. "Face Recognition from Low Resolution Images." In Communications in Computer and Information Science, 220–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30721-8_22.
Повний текст джерелаHernández-Durán, Mairelys, Veronika Cheplygina, and Yenisel Plasencia-Calaña. "Dissimilarity Representations for Low-Resolution Face Recognition." In Similarity-Based Pattern Recognition, 70–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24261-3_6.
Повний текст джерелаGolla, Monika Rani, Poonam Sharma, and Jitendra Madarkar. "Face Recognition Algorithm for Low-Resolution Images." In Social Networking and Computational Intelligence, 349–62. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2071-6_29.
Повний текст джерелаWang, Xiaoying, Le Liu, and Haifeng Hu. "Coupled Kernel Fisher Discriminative Analysis for Low-Resolution Face Recognition." In Biometric Recognition, 81–88. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_10.
Повний текст джерелаWang, Xuebo, Yao Lu, Xiaozhen Chen, Weiqi Li, and Zijian Wang. "Asymmetric Pyramid Based Super Resolution from Very Low Resolution Face Image." In Pattern Recognition and Computer Vision, 694–702. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_59.
Повний текст джерелаKono, Yuki, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, and Hiroshi Murase. "Frontal Face Generation from Multiple Low-Resolution Non-frontal Faces for Face Recognition." In Computer Vision – ACCV 2010 Workshops, 175–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22822-3_18.
Повний текст джерелаArya, K. V., Shyam Singh Rajput, and Shambhavi Upadhyay. "Noise-Robust Low-Resolution Face Recognition Using SIFT Features." In Advances in Intelligent Systems and Computing, 645–55. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1135-2_49.
Повний текст джерелаJia, Guangheng, Xiaoguang Li, Li Zhuo, and Li Liu. "Recognition Oriented Feature Hallucination for Low Resolution Face Images." In Lecture Notes in Computer Science, 275–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48896-7_27.
Повний текст джерелаMudunuri, Sivaram Prasad, Shashanka Venkataramanan, and Soma Biswas. "Improved Low Resolution Heterogeneous Face Recognition Using Re-ranking." In Communications in Computer and Information Science, 446–56. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0020-2_39.
Повний текст джерелаТези доповідей конференцій з теми "Low Resolution Face Recognition"
Wilman, W. W. Zou, and Pong C. Yuen. "Very low resolution face recognition problem." In 2010 IEEE Fourth International Conference On Biometrics: Theory, Applications And Systems (BTAS). IEEE, 2010. http://dx.doi.org/10.1109/btas.2010.5634490.
Повний текст джерелаXu, Yong, and Zhong Jin. "Down-Sampling Face Images and Low-Resolution Face Recognition." In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, 2008. http://dx.doi.org/10.1109/icicic.2008.234.
Повний текст джерелаRajawat, Aparna, Mahendra Kumar Pandey, and Shyam Singh Rajput. "Low resolution face recognition techniques: A survey." In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT). IEEE, 2017. http://dx.doi.org/10.1109/ciact.2017.7977381.
Повний текст джерелаRoshna, N. R., and S. Naveen. "Multimodal low resolution face recognition using SVD." In 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT). IEEE, 2017. http://dx.doi.org/10.1109/iccpct.2017.8074200.
Повний текст джерелаLei, Zhen, Timo Ahonen, Matti Pietikainen, and Stan Z. Li. "Local frequency descriptor for low-resolution face recognition." In Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771391.
Повний текст джерелаWang, Haihan, and Shangfei Wang. "Low-Resolution Face Recognition Enhanced by High-Resolution Facial Images." In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). IEEE, 2023. http://dx.doi.org/10.1109/fg57933.2023.10042552.
Повний текст джерелаLai, Shun-Cheung, Chen-Hang He, and Kin-Man Lam. "Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803782.
Повний текст джерелаHuang, Shih-Ming, Yang-Ting Chou, Szu-Hua Wu, and Jar-Ferr Yang. "Multi-Resolution Local Probabilistic Approach for Low Resolution Face Recognition." In 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI). IEEE, 2011. http://dx.doi.org/10.1109/icbmi.2011.67.
Повний текст джерелаTan, Jin Chyuan, Kian Ming Lim, and Chin Poo Lee. "Enhanced AlexNet with Super-Resolution for Low-Resolution Face Recognition." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527433.
Повний текст джерелаChai, Jacky Chen Long, Cheng Yaw Low, and Andrew Beng Jin Teoh. "DIRA: disjoint-identity resolution adaptation for low-resolution face recognition." In Fourteenth International Conference on Digital Image Processing (ICDIP 2022), edited by Yi Xie, Xudong Jiang, Wenbing Tao, and Deze Zeng. SPIE, 2022. http://dx.doi.org/10.1117/12.2644258.
Повний текст джерелаЗвіти організацій з теми "Low Resolution Face Recognition"
Inter-American Development Bank Group Climate Change Action Plan 2021-2025. Inter-American Development Bank, March 2021. http://dx.doi.org/10.18235/0003153.
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