Готові списки джерел за темами / Facial recognition algorithms

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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Facial recognition algorithms"

1

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.

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Анотація:
The facial recognition system is used to provide identification and authentication during functional testing. It can also be used to identify people in different situations. This article presents a comparative study of the algorithms used for facial isolation and recognition. Algorithms are general algorithms that match a recognizable face. The concept of each algorithm is explained and a corresponding description is given. In addition, the results of the algorithms are evaluated in a data set and are displayed as graphs for evaluating the effectiveness of each algorithm. Algorithms work with a common data set and display the percentage of functions obtained.
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2

Dirin, 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.

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<p class="affiliations"><strong>Abstract— </strong>Computer visions and their applications have become important in contemporary life. Hence, researches on facial and object recognition have become increasingly important both from academicians and practitioners. Smart gadgets such as smartphones are nowadays capable of high processing power, memory capacity, along with high resolutions camera. Furthermore, the connectivity bandwidth and the speed of the interaction have significantly impacted the popularity of mobile object recognition applications. These developments in addition to computer vision’s algorithms advancement have transferred object’s recognitions from desktop environments to the mobile world. The aim of this paper to reveal the efficiency and accuracy of the existing open-source facial recognition algorithms in real-life settings. We use the following popular open-source algorithms for efficiency evaluations: Eigenfaces, Fisherfaces, Local Binary Pattern Histogram, the deep convolutional neural network algorithm, and OpenFace. The evaluations of the test cases indicate that among the compared facial recognition algorithms the OpenFace algorithm has the highest accuracy to identify faces. The findings of this study help the practitioner on their decision of the algorithm selections and the academician on how to improve the accuracy of the current algorithms even further.</p>
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3

Ahmad 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.

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Анотація:
Facial recognition is the method by which an individual's identity is determined by a facial image. With the support of this method, it is potential to use the face image of the person to document it in any safety system. Facial recognition methods for static images can approximately be classified into comprehensive approaches to comprehensive and feature-based approaches. The comprehensive systems use the whole raw face image as input, while feature-based approaches utilize limited face features and use their regular and decorative properties [1]. A huge number of facial recognition systems have been advanced in the past periods. In this paper, we review a varied range of approaches used to identify facial recognition, which contains LDA, PCA, SVM, and ICA.
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4

Popoola, 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.

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Анотація:
Systems and applications embedded with facial detection and recognition capabilities are founded on the notion that there are differences in face structures among individuals, and as such, we can perform face-matching using the facial symmetry. A widely used application of facial detection and recognition is in security. It is important that the images be processed correctly for computer-based facial recognition, hence, the usage of efficient, cost-effective algorithms and a robust database. This research work puts these measures into consideration and attempts to determine a cost-effective and reliable algorithm out of three algorithms examined. Keywords: Haar-Cascade, PCA, Eigenfaces, Fisherfaces, LBPH, Face Recognition.
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5

BUKOWSKI, 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.

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Анотація:
Information technology of the 20th and 21st centuries “opened the way” to the automatic assessment of anthropometric facial features, facial gestures and other characteristic behaviours. Recognition is a very complex technical problem with a signifi cant practical effect. There are dedicated applications for this purpose. The article presents face recognition algorithms for 2D images, for three-dimensional spaces, and methods using neural networks. Linear and nonlinear, local and global, and hybrid methods of facial recognition are presented. The study understands the strengths and weaknesses of the laws governing the use of face recognition technology and, if possible, analyses their effi ciency. The methodological review has been created in connection with the idea of the author’s own fast algorithms and facial recognition.
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6

Zhu, 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.

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Анотація:
The paper proposes a novel discriminant non-negative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional non-negative matrix factorization algorithms, the algorithm adds discriminant constraints in low-dimensional weights. The experiments on facial expression recognition indicate that the algorithm enhances the discrimination capability of low-dimensional features and achieves better performance than other non-negative matrix factorization algorithms.
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7

Costa, 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.

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Анотація:
The advancement in technology in recent decades has provided many facilities for humanity in various applications, and facial recognition technology is one of them. There are several problemsto be solved to perform face recognition from digital images, such as varying ambient lighting, changing the face physical characteristics and resolution of the images used. This work aimed to perform a comparative analysis between some of thedetection and facial recognition methods, as well as their execution time. We use the Eigenface, Fisherface and LBPH facial recognition algorithms in conjunction with the Haar Cascade facedetection algorithm, all from the OpenCV library. We also explored the use of CNN neural network for facial recognition in conjunction with the HOG facial detection algorithm, these from the Dlib library. The work aimed, besides analyzing the algorithms in relation to hit rates, factors such as reliability and execution time were also considered
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8

binghua, 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.

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Анотація:
Aiming at the problem of recognition effect is not stable when 2D facial expression recognition in the complex illumination and posture changes. A facial expression recognition algorithm based on RGB-D dynamic sequence analysis is proposed. The algorithm uses LBP features which are robust to illumination, and adds depth information to study the facial expression recognition. The algorithm firstly extracts 3D texture features of preprocessed RGB-D facial expression sequence, and then uses the CNN to train the dataset. At the same time, in order to verify the performance of the algorithm, a comprehensive facial expression library including 2D image, video and 3D depth information is constructed with the help of Intel RealSense technology. The experimental results show that the proposed algorithm has some advantages over other RGB-D facial expression recognition algorithms in training time and recognition rate, and has certain reference value for future research in facial expression recognition.
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9

a, 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.

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Анотація:
Human expresses their mood and sometimes what they need through their expression. This project traces the mood of the human using a real time recognition system which will detect the emotion. It can be a smiling face, or it can be the face full of anger. Facial emotion recognition is one of the useful task and can be used as a base for many real-time applications. The example can be feedback through moods at any restaurants and hotels about their services and foods. It can be much impactful in the field of military. Its very usage can be helpful for recognizing the people’s behaviour at the border areas to find out the suspects between them. This project consists of various algorithms of machine as well as deep learning. Some of the libraries are: Keras, OpenCV, Matplotlib. Image processing is used in classifying the universal emotions like neutral, surprise, sad, angry, happy, disguist, fear. This project consists of two modules: (i)Processing and generating the model for the application using different algorithms and (ii) Application for using the model using OpenCV to recognize. A set of values obtained after processing those extracted features points are given as input to recognize the emotion. Keywords: facial emotion recognition, deep neural networks, automatic recognition database
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10

Kaur, 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.

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Анотація:
The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accepted biometric method. The domain of face recognition has gained the attention of many scientists, and hence it has become a standard benchmark in the area of human recognition. It has turned out to be the most deeply studied area in computer vision for more than four decades. It has a wide array of applications, including security monitoring, automated surveillance systems, victim and missing-person identification and so on. This review presents the broad range of methods used for face recognition and attempts to discuss their advantages and disadvantages. Initially, we present the basics of face-recognition technology, its standard workflow, background and problems, and the potential applications. Then, face-recognition methods with their advantages and limitations are discussed. The concluding section presents the possibilities and future implications for further advancing the field.
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Більше джерел

Дисертації з теми "Facial recognition algorithms"

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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.

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In this paper an analysis is conducted regarding whether a higher classification accuracy of facial expressions are possible. The approach used is that the seven basic emotional states are combined into a binary classification problem. Five different machine learning algorithms are implemented: Support vector machines, Extreme learning Machine and three different Convolutional Neural Networks (CNN). The utilized CNN:S were one conventional, one based on VGG16 and transfer learning and one based on residual theory known as RESNET50. The experiment was conducted on two datasets, one small containing no contamination called JAFFE and one big containing contamination called FER2013. The highest accuracy was achieved with the CNN:s where RESNET50 had the highest classification accuracy. When comparing the classification accuracy with the state of the art accuracy an improvement of around 0.09 was achieved on the FER2013 dataset. This dataset does however include some ambiguities regarding what facial expression is shown. It would henceforth be of interest to conduct an experiment where humans classify the facial expressions in the dataset in order to achieve a benchmark.
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2

Silva, 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.
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3

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.

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Aim: To evaluate the ability of a publicly available facial recognition application program interface (API) to calculate similarity scores for pre- and post-surgical photographs of patients undergoing orthognathic surgeries. Our primary objective was to identify which surgical procedure(s) had the greatest effect(s) on similarity score. Methods: Standard treatment progress photographs for 25 retrospectively identified, orthodontic-orthognathic patients were analyzed using the API to calculate similarity scores between the pre- and post-surgical photographs. Photographs from two pre-surgical timepoints were compared as controls. Both relaxed and smiling photographs were included in the study to assess for the added impact of facial pose on similarity score. Surgical procedure(s) performed on each patient, gender, age at time of surgery, and ethnicity were recorded for statistical analysis. Nonparametric Kruskal-Wallis Rank Sum Tests were performed to univariately analyze the relationship between each categorical patient characteristic and each recognition score. Multiple comparison Wilcoxon Rank Sum Tests were performed on the subsequent statistically significant characteristics. P-Values were adjusted for using the Bonferroni correction technique. Results: Patients that had surgery on both jaws had a lower median similarity score, when comparing relaxed expressions before and after surgery, compared to those that had surgery only on the mandible (p = 0.014). It was also found that patients receiving LeFort and bilateral sagittal split osteotomies (BSSO) surgeries had a lower median similarity score compared to those that received only BSSO (p = 0.009). For the score comparing relaxed expressions before surgery versus smiling expressions after surgery, patients receiving two-jaw surgeries had lower scores than those that had surgery on only the mandible (p = 0.028). Patients that received LeFort and BSSO surgeries were also found to have lower similarity scores compared to patients that received only BSSO when comparing pre-surgical relaxed photographs to post-surgical smiling photographs (p = 0.036). Conclusions: Two-jaw surgeries were associated with a statistically significant decrease in similarity score when compared to one-jaw procedures. Pose was also found to be a factor influencing similarity scores, especially when comparing pre-surgical relaxed photographs to post-surgical smiling photographs.
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4

Garcia, 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.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
In 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
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5

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.

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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
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
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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.

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Анотація:
Orientador: Anna Diva Plasencia Lotufo
Co-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
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7

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.

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Анотація:
Le trouble du Spectre de l’Autisme (TSA) est caractérisé par des difficultés concernant les habiletés sociales dont l’utilisation des expressions faciales émotionnelles (EFE). Si de nombreuses études s’intéressent à leur reconnaissance, peu évaluent leur production chez l’enfant typique et avec TSA. Les nouvelles technologies sont plébiscitées pour travailler les habiletés sociales auprès des enfants avec TSA, or, peu d’études concernent leur utilisation pour le travail de la production des EFE. Au début de ce projet, nous retrouvions seulement 4 jeux la travaillant. Notre objectif a été la création du jeu sérieux JEMImE travaillant la production des EFE chez l’enfant avec TSA grâce à un feedback automatisé. Nous avons d’abord constitué une base de données d’EFE d’enfants typiques et avec TSA pour créer un algorithme de reconnaissance des EFE et étudier leurs compétences de production. Plusieurs facteurs les influencent comme l’âge, le type d’émotion, la culture. Les EFE des enfants avec TSA sont jugées de moins bonne qualité par des juges humains et par l’algorithme de reconnaissance des EFE qui a besoin de plus de points repères sur leurs visages pour classer leurs EFE. L’algorithme ensuite intégré dans JEMImE donne un retour visuel en temps réel à l’enfant pour corriger ses productions. Une étude pilote auprès de 23 enfants avec TSA met en avant une bonne adaptation des enfants aux retours de l’algorithme ainsi qu’une bonne expérience dans l’utilisation du jeu. Ces résultats prometteurs ouvrent la voie à un développement plus poussé du jeu pour augmenter le temps de jeu et ainsi évaluer l’effet de cet entraînement sur la production des EFE chez les enfants avec TSA
The 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
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8

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.

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Анотація:
La reconnaissance faciale (RF) est un domaine de recherche très actif en raison de ses nombreuses applications dans le domaine de la vision par ordinateur en général et en biométrie en particulier. Cet intérêt est motivé par plusieurs raisons. D'abord, le visage est universel. Ensuite, il est le moyen le plus naturel par les êtres humains de s'identifier les uns des autres. Enfin, le visage en tant que modalité biométrique est présente un caractère non intrusif, ce qui le distingue d'autres modalités biométriques comme l'iris ou l'emprunte digitale. La RF représente aussi des défis scientifiques importants. D'abord parce que tous les visages humains ont des configurations similaires. Ensuite, avec les images faciales 2D que l'on peut acquérir facilement, la variation intra-classe, due à des facteurs comme le changement de poses et de conditions d'éclairage, les variations d'expressions faciales, le vieillissement, est bien plus importante que la variation inter-classe.Avec l'arrivée des systèmes d'acquisition 3D capables de capturer la profondeur d'objets, la reconnaissance faciale 3D (RF 3D) a émergé comme une voie prometteuse pour traiter les deux problèmes non résolus en 2D, à savoir les variations de pose et d'éclairage. En effet, les caméras 3D délivrent généralement les scans 3D de visages avec leurs images de texture alignées. Une solution en RF 3D peut donc tirer parti d'une fusion avisée d'informations de forme en 3D et celles de texture en 2D. En effet, étant donné que les scans 3D de visage offrent à la fois les surfaces faciales pour la modalité 3D pure et les images de texture 2D alignées, le nombre de possibilités de fusion pour optimiser le taux de reconnaissance est donc considérable. L'optimisation de stratégies de fusion pour une meilleure RF 3D est l'objectif principal de nos travaux de recherche menés dans cette thèse.Dans l'état d'art, diverses stratégies de fusion ont été proposées pour la reconnaissance de visages 3D, allant de la fusion précoce "early fusion" opérant au niveau de caractéristiques à la fusion tardive "late fusion" sur les sorties de classifieurs, en passant par de nombreuses stratégies intermédiaires. Pour les stratégies de fusion tardive, nous distinguons encore des combinaisons en parallèle, en cascade ou multi-niveaux. Une exploration exhaustive d'un tel espace étant impossible, il faut donc recourir à des solutions heuristiques qui constituent nos démarches de base dans le cadre des travaux de cette thèse.En plus, en s'inscrivant dans un cadre de systèmes biométriques, les critères d'optimalité des stratégies de fusion restent des questions primordiales. En effet, une stratégie de fusion est dite optimisée si elle est capable d'intégrer et de tirer parti des différentes modalités et, plus largement, des différentes informations extraites lors du processus de reconnaissance quelque soit leur niveau d'abstraction et, par conséquent, de difficulté.Pour surmonter toutes ces difficultés et proposer une solution optimisée, notre démarche s'appuie d'une part sur l'apprentissage qui permet de qualifier sur des données d'entrainement les experts 2D ou 3D, selon des critères de performance comme ERR, et d'autre part l'utilisation de stratégie d'optimisation heuristique comme le recuit simulé qui permet d'optimiser les mélanges des experts à fusionner. [...]
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9

Surajpal, Dhiresh Ramchander. "An independent evaluation of subspace facial recognition algorithms." Thesis, 2008. http://hdl.handle.net/10539/5906.

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In traversing the diverse field of biometric security and face recognition techniques, this investigation explores a rather rare comparative study of three of the most popular Appearance-based Face Recognition projection classes, these being the methodologies of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). Both the linear and kernel alternatives are investigated along with the four most widely accepted similarity measures of City Block (L1), Euclidean (L2), Cosine and the Mahalanobis metrics. Although comparisons between these classes can become fairly complex given the different task natures, the algorithm architectures and the distance metrics that must be taken into account, an important aspect of this study is the completely equal working conditions that are provided in order to facilitate fair and proper comparative levels of evaluation. In doing so, one is able to realise an independent study that significantly contributes to prior literary findings, either by verifying previous results, offering further insight into why certain conclusions were made or by providing a better understanding as to why certain claims should be disputed and under which conditions they may hold true. The experimental procedure examines ten algorithms in the categories of expression, illumination, occlusion and temporal delay; the results are then evaluated based on a sequential combination of assessment tools that facilitate both intuitive and statistical decisiveness among the intra and inter-class comparisons. In a bid to boost the overall efficiency and accuracy levels of the identification system, the ‘best’ categorical algorithms are then incorporated into a hybrid methodology, where the advantageous effects of fusion strategies are considered. This investigation explores the weighted-sum approach, which by fusion at a matching score level, effectively harnesses the complimentary strengths of the component algorithms and in doing so highlights the improved performance levels that can be provided by hybrid implementations. In the process, by firstly exploring previous literature with respect to each other and secondly by relating the important findings of this paper to previous works one is also able to meet the primary objective in providing an amateur with a very insightful understanding of publicly available subspace techniques and their comparable application status within the environment of face recognition.
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10

Watkins, Elizabeth Anne. "The Polysemia of Recognition: Facial Recognition in Algorithmic Management." Thesis, 2021. https://doi.org/10.7916/d8-6qwc-0t83.

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Algorithmic management systems organize many different kinds of work across domains, and have increasingly come under academic scrutiny. Under labels including gig work, piecemeal work, and platform labor, these systems have been richly theorized under disciplines including human-computer interaction, sociology, communications, economics, and labor law. When it comes to the relationships between such systems and their workers, current theory frames these interactions on a continuum between organizational control and worker autonomy. This has laid the groundwork for other ways of examining micro-level practices of workers under algorithmic management. As an alternative to the binary of control and autonomy, this dissertation takes its cue from feminist scholars in Science, Technology, and Society (STS) studies. Drawing on frameworks from articulation, repair, and mutual shaping, I examine workers’ interpretations and interactions, to ask how new subjectivities around identity and community emerge from these entanglements. To shed empirical light on these processes, this dissertation employs a mixed-methods research design examining the introduction of facial recognition into the sociotechnical systems of algorithmic management. Data include 22 in-person interviews with workers in New York City and Toronto, a survey of 100 workers in the United States who have been subjected to facial recognition, and analysis of over 2800 comments gathered from an online workers’ forum posted over the course of four years.Facial recognition, like algorithmic management, suffers from a lack of empirical, on-the-ground insights into how workers communicate, negotiate, and strategize around and through them. Interviews with workers reveals that facial recognition evokes polysemia, i.e. a number of distinct, yet interrelated interpretations. I find that for some workers, facial recognition means safety and security. To others it means violation of privacy and accusations of fraud. Some are impressed by the “science-fiction”-like capabilities of the system: “it’s like living in the future.” Others are wary, and science fiction becomes a vehicle to encapsulate their fears: “I’m in the [movie] The Minority Report.” For some the technology is hyper-powerful: “It feels like I’m always being watched,” yet others decry, “it’s an obvious façade.” Following interviews, I build a body of research using empirical methods combined with frameworks drawn from STS and organizational theory to illuminate workers’ perceptions and strategies negotiating their algorithmic managers. I operationalize Julian Orr’s studies of storytelling among Xerox technicians to analyze workers’ information-sharing practices in online forums, to better understand how gig workers, devices, forums, and algorithmic management systems engage in mutual shaping processes. Analysis reveals that opposing interpretations of facial recognition, rather than dissolving into consensus of “shared understanding,” continue to persist. Rather than pursuing and relying on shared understanding of their work to maintain relationships, workers under algorithmic management, communicating in online forums about facial recognition, elide consensus. After forum analysis, I then conduct a survey, to assess workers’ fairness perceptions of facial recognition targeting and verification. The goal of this research is to establish an empirical foundation to determine whether algorithmic fairness perceptions are subject to theories of bounded rationality and decision-making. Finally, for the last two articles, I turn back to the forums, to analyze workers’ experiences negotiating two other processes with threats or ramifications for safety, privacy, and risk. In one article, I focus on their negotiation of threats from scam attackers, and the use the forum itself as a “shared repertoire” of knowledge. In the other I use the forums as evidence to illuminate workers’ experiences and meaning-making around algorithmic risk management under COVID-19. In the conclusion, I engage in theory-building to examine how algorithmic management and its attendant processes demand that information-sharing mechanisms serve novel ends buttressing legitimacy and authenticity, in what I call “para-organizational” work, a world of work where membership and legitimacy are liminal and uncertain. Ultimately, this body of research illuminates mutual shaping processes in which workers’ practices, identity, and community are entangled with technological artifacts and organizational structures. Algorithmic systems of work and participants’ interpretations of, and interactions with, related structures and devices, may be creating a world where sharing information is a process wielded not as a mechanism of learning, but as one of belonging.
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Книги з теми "Facial recognition algorithms"

1

Aradau, Claudia, and Tobias Blanke. Algorithmic Reason. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192859624.001.0001.

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Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences.
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2

Bindemann, Markus, ed. Forensic Face Matching. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198837749.001.0001.

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Person identification at passport control, at borders, in police investigations, and in criminal trials relies critically on the identity verification of people via image-to-image or person-to-image comparison. While this task is known as ‘facial image comparison’ in forensic settings, it has been studied as ‘unfamiliar face matching’ in cognitive science. This book brings together expertise from practitioners, and academics in psychology and law, to draw together what is currently known about these tasks. It explains the problem of identity impostors and how within-person variability and between-person similarity, due to factors such as image quality, lighting direction, and view, affect identification. A framework to develop a cognitive theory of face matching is offered. The face-matching abilities of untrained lay observers, facial reviewers, facial examiners, and super-recognizers are analysed and contrasted. Individual differences between observers, learning and training for face recognition and face matching, and personnel selection are reviewed. The admissibility criteria of evidence from face matching in legal settings are considered, focusing on aspects such as the requirement of relevance, the prohibition on evidence of opinion, and reliability. Key concepts relevant to automatic face recognition algorithms at airports and in police investigations are explained, such as deep convolutional neural networks, biometrics, and human–computer interaction. Finally, new security threats in the form of hyper-realistic mask disguises are considered, including the impact these have on person identification in applied and laboratory settings.
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Частини книг з теми "Facial recognition algorithms"

1

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.

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Ramya, 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.

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3

Tiwari, 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.

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4

Misra, 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.

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5

Chand, 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.

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6

Henry, 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.

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7

Dey, 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.

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8

Chander, 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.

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9

Jacob, 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.

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10

Carreño, 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.

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Тези доповідей конференцій з теми "Facial recognition algorithms"

1

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.

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Feng, 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.

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Xiong, 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.

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4

Singh, 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.

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5

Chengeta, 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.

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6

Canedo, 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.

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7

Baculo, 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.

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8

Siddiqui, 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.

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Abbo, 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.

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Levchuk, 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.

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Звіти організацій з теми "Facial recognition algorithms"

1

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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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.
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