Academic literature on the topic 'Facial recognition algorithms'
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Journal articles on the topic "Facial recognition algorithms"
Malikova, F. U., N. ZH Zhanat, A. K. Saginayeva, and R. S. Ryskeldy. "FEATURES OF FACIAL RECOGNITION." BULLETIN Series of Physics & Mathematical Sciences 69, no. 1 (March 10, 2020): 374–77. http://dx.doi.org/10.51889/2020-1.1728-7901.67.
Full textDirin, Amir, Nicolas Delbiaggio, and Janne Kauttonen. "Comparisons of Facial Recognition Algorithms Through a Case Study Application." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 14 (August 28, 2020): 121. http://dx.doi.org/10.3991/ijim.v14i14.14997.
Full textAhmad Khorsheed, Eman, and Zakiya Ali Nayef. "Face Recognition Algorithms: A Review." Academic Journal of Nawroz University 11, no. 3 (August 1, 2022): 202–7. http://dx.doi.org/10.25007/ajnu.v11n3a1432.
Full textPopoola, J. A., and C. O. Yinka-Banjo. "Comparative analysis of selected facial recognition algorithms." Nigerian Journal of Technology 39, no. 3 (September 16, 2020): 896–904. http://dx.doi.org/10.4314/njt.v39i3.31.
Full textBUKOWSKI, MICHAŁ. "REVIEW OF FACE RECOGNITION ALGORITHMS." PRZEGLĄD POLICYJNY 140, no. 4 (March 17, 2021): 209–43. http://dx.doi.org/10.5604/01.3001.0014.8469.
Full textZhu, Yan Li, Jun Chen, and Pei Xin Qu. "A Novel Discriminant Non-Negative Matrix Factorization and its Application to Facial Expression Recognition." Advanced Materials Research 143-144 (October 2010): 129–33. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.129.
Full textCosta, Lucas José da, Thiago Luz de Sousa, Francisco Assis da Silva, Leandro Luiz de Almeida, Danillo Roberto Pereira, Almir Olivette Artero, and Marco Antonio Piteri. "ANÁLISE DE MÉTODOS DE DETECÇÃO E RECONHECIMENTO DE FACES UTILIZANDO VISÃO COMPUTACIONAL E ALGORITMOS DE APRENDIZADO DE MÁQUINA." COLLOQUIUM EXACTARUM 13, no. 2 (September 22, 2021): 01–11. http://dx.doi.org/10.5747/ce.2021.v13.n2.e354.
Full textbinghua, HE, CHEN zengzhao, LI gaoyang, JIANG lang, ZHANG zhao, and DENG chunlin. "An expression recognition algorithm based on convolution neural network and RGB-D Images." MATEC Web of Conferences 173 (2018): 03066. http://dx.doi.org/10.1051/matecconf/201817303066.
Full texta, Vinayak, and Rachana R. Babu. "Facial Emotion Recognition." YMER Digital 21, no. 05 (May 23, 2022): 1010–15. http://dx.doi.org/10.37896/ymer21.05/b5.
Full textKaur, Paramjit, Kewal Krishan, Suresh K. Sharma, and Tanuj Kanchan. "Facial-recognition algorithms: A literature review." Medicine, Science and the Law 60, no. 2 (January 21, 2020): 131–39. http://dx.doi.org/10.1177/0025802419893168.
Full textDissertations / Theses on the topic "Facial recognition algorithms"
Nordén, Frans, and Reis Marlevi Filip von. "A Comparative Analysis of Machine Learning Algorithms in Binary Facial Expression Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254259.
Full textSilva, Eduardo Machado. "Padrões mapeados localmente em multiescala aplicados ao reconhecimento de faces." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/154142.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
O Reconhecimento facial é uma das tecnologias biométricas mais utilizadas em sistemas automatizados que necessitam garantir a identidade de uma pessoa para acesso autorizado e monitoramento. A grande aceitação do uso da face tem várias vantagens sobre outras tecnologias biométricas: ela é natural, não exige equipamentos sofisticados, a aquisição de dados é baseada em abordagens não invasivas, e pode ser feito a distância, de maneira cooperativa ou não. Embora muitos estudos em reconhecimento facial tenham sido feitos, problemas com variação de iluminação, poses com oclusão facial, expressão facial e envelhecimento ainda são desafios, pois influenciam a performance dos sistemas de reconhecimento facial e motivam o desenvolvimento de novos sistemas de reconhecimento que lidam com esses problemas e sejam mais confiáveis. Este trabalho tem como objetivo avaliar a técnica de Padrões Localmente Mapeados em Multiescala (MSLMP) para o reconhecimento facial. Técnicas baseadas em algoritmos genéticos e processamento de imagens foram usadas para obter melhores resultados. Os resultados obtidos chegam a 100% de acurácia para alguns banco de dados. A base de dados MUCT ´e, em particular, bastante complexa, ela foi criada em 2010 com o objetivo de aumentar a quantidade de bancos de dados disponíveis com alta variação de iluminação, idade, posições e etnias, e por isso, ´e um banco de dados difícil quanto ao reconhecimento automático de faces. Uma nova técnica de processamento baseada na média dos níveis de cinza da base foi desenvolvida.
Facial recognition is one of the most used biometric technologies in automated systems which ensure a person’s identity for authorized access and monitoring. The acceptance of face use has several advantages over other biometric technologies: it is natural, it does not require sophisticated equipment, data acquisition is based on non-invasive approaches, and can it be done remotely, cooperatively or not. Although many facial recognition studies have been done, problems with light variation, facial occlusion, position, expression, and aging are still challenges, because they influence the performance of facial recognition systems and motivate the development of more reliable recognition systems that deal with these problems. This work aim to evaluate the Multi-scale Local Mapped Pattern (MSLMP) technique for the facial recognition. Techniques based on genetic algorithms and image processing were applied to increase the performance of the method. The obtained results reach up to 100% of accuracy for some databases. A very difficult database to deal is the MUCT database which was created in 2010 with aim of providing images with high variation of lighting, age, positions and ethnicities in the facial biometry literature, which makes it a highly difficult base in relation to automated recognition. A new processing technique was developed based on the average gray levels of the images of the database.
Dragon, Carolyn Bradford. "Let’s Face It: The effect of orthognathic surgery on facial recognition algorithm analysis." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5778.
Full textGarcia, Ivette Cristina Araujo, Eduardo Rodrigo Linares Salmon, Rosario Villalta Riega, and Alfredo Barrientos Padilla. "Implementation and customization of a smart mirror through a facial recognition authentication and a personalized news recommendation algorithm." Institute of Electrical and Electronics Engineers Inc, 2018. http://hdl.handle.net/10757/624657.
Full textIn recent years the advancement of technologies of information and communication (technology ICTs) have helped to improve the quality of people's lives. The paradigm of internet of things (IoT, Internet of things) presents innovative solutions that are changing the style of life of the people. Because of this proposes the implementation of a smart mirror as part of a system of home automation, with which we intend to optimize the time of people as they prepare to start their day. This device is constructed from a reflective glass, LCD monitor, a Raspberry Pi 3, a camera and a platform IoT oriented cloud computing, where the information is obtained to show in the mirror, through the consumption of web services. The information is customizable thanks to a mobile application, which in turn allows the user photos to access the mirror, using authentication with facial recognition and user information to predict the news to show according to your profile. In addition, as part of the idea of providing the user a personalized experience, the Smart Mirror incorporates a news recommendation algorithm, implemented using a predictive model, which uses the algorithm, naive bayes.
Revisión por pares
Silva, Jadiel Caparrós da [UNESP]. "Aplicação de sistemas imunológicos artificiais para biometria facial: Reconhecimento de identidade baseado nas características de padrões binários." Universidade Estadual Paulista (UNESP), 2015. http://hdl.handle.net/11449/127901.
Full textO presente trabalho tem como objetivo realizar o reconhecimento de identidade por meio de um método baseado nos Sistemas Imunológicos Artificiais de Seleção Negativa. Para isso, foram explorados os tipos de recursos e alternativas adequadas para a análise de expressões faciais 3D, abordando a técnica de Padrão Binário que tem sido aplicada com sucesso para o problema 2D. Inicialmente, a geometria facial 3D foi convertida em duas representações em 2D, a Depth Map e a APDI, que foram implementadas com uma variedade de tipos de recursos, tais como o Local Phase Quantisers, Gabor Filters e Monogenic Filters, a fim de produzir alguns descritores para então fazer-se a análise de expressões faciais. Posteriormente, aplica-se o Algoritmo de Seleção Negativa onde são realizadas comparações e análises entre as imagens e os detectores previamente criados. Havendo afinidade entre as imagens previamente estabelecidas pelo operador, a imagem é classificada. Esta classificação é chamada de casamento. Por fim, para validar e avaliar o desempenho do método foram realizados testes com imagens diretamente da base de dados e posteriormente com dez descritores desenvolvidos a partir dos padrões binários. Esses tipos de testes foram realizados tendo em vista três objetivos: avaliar quais os melhores descritores e as melhores expressões para se realizar o reconhecimento de identidade e, por fim, validar o desempenho da nova solução de reconhecimento de identidades baseado nos Sistemas Imunológicos Artificiais. Os resultados obtidos pelo método apresentaram eficiência, robustez e precisão no reconhecimento de identidade facial
This work aims to perform the identity recognition by a method based on Artificial Immune Systems, the Negative Selection Algorithm. Thus, the resources and adequate alternatives for analyzing 3D facial expressions were explored, exploring the Binary Pattern technique that is successfully applied for the 2D problem. Firstly, the 3D facial geometry was converted in two 2D representations. The Depth Map and the Azimuthal Projection Distance Image were implemented with other resources such as the Local Phase Quantisers, Gabor Filters and Monogenic Filters to produce descriptors to perform the facial expression analysis. Afterwards, the Negative Selection Algorithm is applied, and comparisons and analysis with the images and the detectors previously created are done. If there is affinity with the images, than the image is classified. This classification is called matching. Finally, to validate and evaluate the performance of the method, tests were realized with images from the database and after with ten descriptors developed from the binary patterns. These tests aim to: evaluate which are the best descriptors and the best expressions to recognize the identities, and to validate the performance of the new solution of identity recognition based on Artificial Immune Systems. The results show efficiency, robustness and precision in recognizing facial identity
Silva, Jadiel Caparrós da. "Aplicação de sistemas imunológicos artificiais para biometria facial: Reconhecimento de identidade baseado nas características de padrões binários /." Ilha Solteira, 2015. http://hdl.handle.net/11449/127901.
Full textCo-orientador: Jorge Manuel M. C. Pereira Batista
Banca: Carlos Roberto Minussi
Banca: Ricardo Luiz Barros de Freitas
Banca: Díbio Leandro Borges
Banca: Gelson da Cruz Junior
Resumo: O presente trabalho tem como objetivo realizar o reconhecimento de identidade por meio de um método baseado nos Sistemas Imunológicos Artificiais de Seleção Negativa. Para isso, foram explorados os tipos de recursos e alternativas adequadas para a análise de expressões faciais 3D, abordando a técnica de Padrão Binário que tem sido aplicada com sucesso para o problema 2D. Inicialmente, a geometria facial 3D foi convertida em duas representações em 2D, a Depth Map e a APDI, que foram implementadas com uma variedade de tipos de recursos, tais como o Local Phase Quantisers, Gabor Filters e Monogenic Filters, a fim de produzir alguns descritores para então fazer-se a análise de expressões faciais. Posteriormente, aplica-se o Algoritmo de Seleção Negativa onde são realizadas comparações e análises entre as imagens e os detectores previamente criados. Havendo afinidade entre as imagens previamente estabelecidas pelo operador, a imagem é classificada. Esta classificação é chamada de casamento. Por fim, para validar e avaliar o desempenho do método foram realizados testes com imagens diretamente da base de dados e posteriormente com dez descritores desenvolvidos a partir dos padrões binários. Esses tipos de testes foram realizados tendo em vista três objetivos: avaliar quais os melhores descritores e as melhores expressões para se realizar o reconhecimento de identidade e, por fim, validar o desempenho da nova solução de reconhecimento de identidades baseado nos Sistemas Imunológicos Artificiais. Os resultados obtidos pelo método apresentaram eficiência, robustez e precisão no reconhecimento de identidade facial
Abstract: This work aims to perform the identity recognition by a method based on Artificial Immune Systems, the Negative Selection Algorithm. Thus, the resources and adequate alternatives for analyzing 3D facial expressions were explored, exploring the Binary Pattern technique that is successfully applied for the 2D problem. Firstly, the 3D facial geometry was converted in two 2D representations. The Depth Map and the Azimuthal Projection Distance Image were implemented with other resources such as the Local Phase Quantisers, Gabor Filters and Monogenic Filters to produce descriptors to perform the facial expression analysis. Afterwards, the Negative Selection Algorithm is applied, and comparisons and analysis with the images and the detectors previously created are done. If there is affinity with the images, than the image is classified. This classification is called matching. Finally, to validate and evaluate the performance of the method, tests were realized with images from the database and after with ten descriptors developed from the binary patterns. These tests aim to: evaluate which are the best descriptors and the best expressions to recognize the identities, and to validate the performance of the new solution of identity recognition based on Artificial Immune Systems. The results show efficiency, robustness and precision in recognizing facial identity
Doutor
Grossard, Charline. "Evaluation et rééducation des expressions faciales émotionnelles chez l’enfant avec TSA : le projet JEMImE Serious games to teach social interactions and emotions to individuals with autism spectrum disorders (ASD) Children facial expression production : influence of age, gender, emotion subtype, elicitation condition and culture." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS625.
Full textThe autism spectrum disorder (ASD) is characterized by difficulties in socials skills, as emotion recognition and production. Several studies focused on emotional facial expressions (EFE) recognition, but few worked on its production, either in typical children or in children with ASD. Nowadays, information and communication technologies are used to work on social skills in ASD but few studies using these technologies focus on EFE production. After a literature review, we found only 4 games regarding EFE production. Our final goal was to create the serious game JEMImE to work on EFE production with children with ASD using an automatic feedback. We first created a dataset of EFE of typical children and children with ASD to train an EFE recognition algorithm and to study their production skills. Several factors modulate them, such as age, type of emotion or culture. We observed that human judges and the algorithm assess the quality of the EFE of children with ASD as poorer than the EFE of typical children. Also, the EFE recognition algorithm needs more features to classify their EFE. We then integrated the algorithm in JEMImE to give the child a visual feedback in real time to correct his/her productions. A pilot study including 23 children with ASD showed that children are able to adapt their productions thanks to the feedback given by the algorithm and illustrated an overall good subjective experience with JEMImE. The beta version of JEMImE shows promising potential and encourages further development of the game in order to offer longer game exposure to children with ASD and so allow a reliable assessment of the effect of this training on their production of EFE
Ben, Soltana Wael. "Optimisation de stratégies de fusion pour la reconnaissance de visages 3D." Phd thesis, Ecole Centrale de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-01070638.
Full textSurajpal, Dhiresh Ramchander. "An independent evaluation of subspace facial recognition algorithms." Thesis, 2008. http://hdl.handle.net/10539/5906.
Full textWatkins, Elizabeth Anne. "The Polysemia of Recognition: Facial Recognition in Algorithmic Management." Thesis, 2021. https://doi.org/10.7916/d8-6qwc-0t83.
Full textBooks on the topic "Facial recognition algorithms"
Aradau, Claudia, and Tobias Blanke. Algorithmic Reason. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192859624.001.0001.
Full textBindemann, Markus, ed. Forensic Face Matching. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198837749.001.0001.
Full textBook chapters on the topic "Facial recognition algorithms"
Nannapaneni, Rajasekhar, and Subarna Chatterjee. "Human Emotion Recognition Through Facial Expressions." In Algorithms for Intelligent Systems, 513–25. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4893-6_44.
Full textRamya, V. V. S. S., Shaik Afifa Reshma, Afrah Samreen, and U. Chandrasekhar. "Facial Emotion Recognition Using ML Algorithms." In Lecture Notes in Networks and Systems, 389–402. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7657-4_32.
Full textTiwari, Kamlesh, and Mayank Patel. "Facial Expression Recognition Using Random Forest Classifier." In Algorithms for Intelligent Systems, 121–30. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1059-5_15.
Full textMisra, Nirmalya, Sreejit Ray, Subhajit Pal, and Ruchira Dey. "Facial Recognition-Based Automated Classroom Attendance System." In Algorithms for Intelligent Systems, 439–47. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1657-1_38.
Full textChand, Shruti, Apoorva Singh, Ria Bhatia, Ishween Kaur, and K. R. Seeja. "Real-Time Facial Emotion Recognition Using Deep Learning." In Algorithms for Intelligent Systems, 219–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1295-4_23.
Full textHenry, Rayner Pailus, and Rayner Alfred. "Synergy in Facial Recognition Extraction Methods and Recognition Algorithms." In Lecture Notes in Electrical Engineering, 358–69. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8276-4_34.
Full textDey, Aniruddha, Shiladitya Chowdhury, Jamuna Kanta Sing, Dipak Kumar Basu, and Mita Nasipuri. "An Efficient Face Recognition Method by Fusing Spatial Discriminant Facial Features." In Applied Algorithms, 277–86. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04126-1_24.
Full textChander, Ashish, R. Shrai Lakshman, S. P. Shreyank D. Jain, N. Ravi Prakash, and K. Panimozhi. "Smart Surveillance with Facial Recognition Using Inception Resnet-V1." In Algorithms for Intelligent Systems, 331–41. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3311-0_28.
Full textJacob, Jeena, and J. Jeba Sonia. "Video-Based Facial Expression Recognition: A Deep Learning Approach." In Algorithms for Intelligent Systems, 133–43. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2109-3_12.
Full textCarreño, David, and Xavier Ginesta. "Facial image recognition using neural networks and genetic algorithms." In Computer Analysis of Images and Patterns, 605–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63460-6_169.
Full textConference papers on the topic "Facial recognition algorithms"
Wang, Yu, XinMin Xu, and Yao Zhuang. "Learning Dynamics for Video Facial Expression Recognition." In ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3508546.3508581.
Full textFeng, Fangyu, Xiaoshu Luo, and Guangyu Wang. "A face cropping strategy for facial expression recognition." In Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), edited by Kannimuthu Subramaniyam. SPIE, 2022. http://dx.doi.org/10.1117/12.2661078.
Full textXiong, Hanying, Tongwei Lu, and Hongzhi Zhang. "Real-time Efficient Facial Landmark Detection Algorithms." In AIPR 2020: 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3430199.3430200.
Full textSingh, Seema, R. Ramya, V. Sushma, S. R. Roshini, and R. Pavithra. "Facial Recognition using Machine Learning Algorithms on Raspberry Pi." In 2019 4th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT). IEEE, 2019. http://dx.doi.org/10.1109/iceeccot46775.2019.9114716.
Full textChengeta, Kennedy, and Serestina Viriri. "Facial Expression Recognition using Local Directional Pattern variants and Deep Learning." In ACAI 2018: 2018 International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3302425.3302427.
Full textCanedo, Daniel, and António Neves. "The impact of pre-processing algorithms in facial expression recognition." In Thirteenth International Conference on Machine Vision, edited by Wolfgang Osten, Jianhong Zhou, and Dmitry P. Nikolaev. SPIE, 2021. http://dx.doi.org/10.1117/12.2587865.
Full textBaculo, Maria Jeseca, and Judith Azcarraga. "Emotion Recognition on Selected Facial Landmarks Using Supervised Learning Algorithms." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00258.
Full textSiddiqui, Nyle, Thomas Reither, Rushit Dave, Dylan Black, Tyler Bauer, and Mitchell Hanson. "A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation." In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00020.
Full textAbbo, A. A., V. Jeanne, M. Ouwerkerk, C. Shan, R. Braspenning, A. Ganesh, and H. Corporaal. "Mapping facial expression recognition algorithms on a low-power smart camera." In 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC). IEEE, 2008. http://dx.doi.org/10.1109/icdsc.2008.4635726.
Full textLevchuk, Sofia A., and Alexander Yakimenko. "Stand for Experimental Evaluation of the Quality of Facial Recognition Algorithms." In 2020 1st International Conference Problems of Informatics, Electronics, and Radio Engineering (PIERE). IEEE, 2020. http://dx.doi.org/10.1109/piere51041.2020.9314642.
Full textReports on the topic "Facial recognition algorithms"
Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.
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