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Добірка наукової літератури з теми "FACE RECOGNITION TECHNIQUES"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "FACE RECOGNITION TECHNIQUES"

1

Priya, B. Lakshmi, and Dr M. Pushpa Rani Rani. "Face Recognition System Techniques and Approaches." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 109–13. http://dx.doi.org/10.15373/2249555x/apr2014/32.

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2

Romanyuk, Olexandr N., Sergey I. Vyatkin, Sergii V. Pavlov, Pavlo I. Mykhaylov, Roman Y. Chekhmestruk, and Ivan V. Perun. "FACE RECOGNITION TECHNIQUES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 1 (March 30, 2020): 52–57. http://dx.doi.org/10.35784/iapgos.922.

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Анотація:
The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.
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3

Kushwah, Prashant. "FACE RECOGNITION WITH HYBRID TECHNIQUES." International Journal of Engineering Technologies and Management Research 5, no. 2 (May 1, 2020): 178–87. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.642.

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Face recognition framework is still in test by numerous applications particularly in close perception and in security frameworks. Generally all utilizations of face recognition utilize enormous information sets, making challenges in present time preparing and effectiveness. This paper contains a structure to enhance face recognition framework which have a few phases. For good result in face recognition framework a few upgrades are critical at each stage. A novel plan is displayed in this paper which gives the better execution for face recognition framework. This plan incorporates expanding in datasets, particularly huge datasets which are required for profound learning. Changing the picture differentiate proportion and pivoting the picture at a few edges which can enhance the recognition precision. At that point, trimming the proper territory of face for highlight extraction and getting the best element vector for face recognition finally. The last after effect of this plan will demonstrate that the given structure is able for distinguishing and perceiving faces with various postures, foundations, and appearance in genuine or present time.
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4

jayakumari, V. Vi. "Face Recognition Techniques: A Survey." World Journal of Computer Application and Technology 1, no. 2 (September 2013): 41–50. http://dx.doi.org/10.13189/wjcat.2013.010204.

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5

Kaur, Mandeep, and Jasjit Kaur. "Review of Face Recognition Techniques." International Journal of Computer Applications 164, no. 6 (April 17, 2017): 31–35. http://dx.doi.org/10.5120/ijca2017913731.

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6

Taqdir, Taqdir, and Dr Renu Dhir. "Face Recognition Techniques - A Review." IOSR Journal of Computer Engineering 16, no. 2 (2014): 23–27. http://dx.doi.org/10.9790/0661-162102327.

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7

Somani, Ms Jayashree S. "Face Recognition Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 2498–502. http://dx.doi.org/10.22214/ijraset.2019.6420.

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8

Noori Hashim, Asaad, and Nedaa Kream Shalan. "Face Recognition using Hybrid Techniques." Journal of Engineering and Applied Sciences 14, no. 12 (December 10, 2019): 4158–63. http://dx.doi.org/10.36478/jeasci.2019.4158.4163.

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9

Solanki, Kamini, and Prashant Pittalia. "Review of Face Recognition Techniques." International Journal of Computer Applications 133, no. 12 (January 15, 2016): 20–24. http://dx.doi.org/10.5120/ijca2016907994.

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10

Kachare, Nilima B., and Vandana S. Inamdar. "Survey of Face Recognition Techniques." International Journal of Computer Applications 1, no. 19 (February 25, 2010): 30–34. http://dx.doi.org/10.5120/408-604.

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Дисертації з теми "FACE RECOGNITION TECHNIQUES"

1

Ebrahimpour-Komleh, Hossein. "Fractal techniques for face recognition." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16289/1/Hossein_Ebrahimpour-Komleh_Thesis.pdf.

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Анотація:
Fractals are popular because of their ability to create complex images using only several simple codes. This is possible by capturing image redundancy and presenting the image in compressed form using the self similarity feature. For many years fractals were used for image compression. In the last few years they have also been used for face recognition. In this research we present new fractal methods for recognition, especially human face recognition. This research introduces three new methods for using fractals for face recognition, the use of fractal codes directly as features, Fractal image-set coding and Subfractals. In the first part, the mathematical principle behind the application of fractal image codes for recognition is investigated. An image Xf can be represented as Xf = A x Xf + B which A and B are fractal parameters of image Xf . Different fractal codes can be presented for any arbitrary image. With the defnition of a fractal transformation, T(X) = A(X - Xf ) + Xf , we can define the relationship between any image produced in the fractal decoding process starting with any arbitrary image X0 as Xn = Tn(X) = An(X - Xf ) + Xf . We show that some choices for A or B lead to faster convergence to the final image. Fractal image-set coding is based on the fact that a fractal code of an arbitrary gray-scale image can be divided in two parts - geometrical parameters and luminance parameters. Because the fractal codes for an image are not unique, we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters - which are faster to compute. For recognition purposes, the fractal code of a query image is applied to all the images in the training set for one iteration. The distance between an image and the result after one iteration is used to define a similarity measure between this image and the query image. The fractal code of an image is a set of contractive mappings each of which transfer a domain block to its corresponding range block. The distribution of selected domain blocks for range blocks in an image depends on the content of image and the fractal encoding algorithm used for coding. A small variation in a part of the input image may change the contents of the range and domain blocks in the fractal encoding process, resulting in a change in the transformation parameters in the same part or even other parts of the image. A subfractal is a set of fractal codes related to range blocks of a part of the image. These codes are calculated to be independent of other codes of the other parts of the same image. In this case the domain blocks nominated for each range block must be located in the same part of the image which the range blocks come from. The proposed fractal techniques were applied to face recognition using the MIT and XM2VTS face databases. Accuracies of 95% were obtained with up to 156 images.
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2

Ebrahimpour-Komleh, Hossein. "Fractal techniques for face recognition." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16289/.

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Анотація:
Fractals are popular because of their ability to create complex images using only several simple codes. This is possible by capturing image redundancy and presenting the image in compressed form using the self similarity feature. For many years fractals were used for image compression. In the last few years they have also been used for face recognition. In this research we present new fractal methods for recognition, especially human face recognition. This research introduces three new methods for using fractals for face recognition, the use of fractal codes directly as features, Fractal image-set coding and Subfractals. In the first part, the mathematical principle behind the application of fractal image codes for recognition is investigated. An image Xf can be represented as Xf = A x Xf + B which A and B are fractal parameters of image Xf . Different fractal codes can be presented for any arbitrary image. With the defnition of a fractal transformation, T(X) = A(X - Xf ) + Xf , we can define the relationship between any image produced in the fractal decoding process starting with any arbitrary image X0 as Xn = Tn(X) = An(X - Xf ) + Xf . We show that some choices for A or B lead to faster convergence to the final image. Fractal image-set coding is based on the fact that a fractal code of an arbitrary gray-scale image can be divided in two parts - geometrical parameters and luminance parameters. Because the fractal codes for an image are not unique, we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters - which are faster to compute. For recognition purposes, the fractal code of a query image is applied to all the images in the training set for one iteration. The distance between an image and the result after one iteration is used to define a similarity measure between this image and the query image. The fractal code of an image is a set of contractive mappings each of which transfer a domain block to its corresponding range block. The distribution of selected domain blocks for range blocks in an image depends on the content of image and the fractal encoding algorithm used for coding. A small variation in a part of the input image may change the contents of the range and domain blocks in the fractal encoding process, resulting in a change in the transformation parameters in the same part or even other parts of the image. A subfractal is a set of fractal codes related to range blocks of a part of the image. These codes are calculated to be independent of other codes of the other parts of the same image. In this case the domain blocks nominated for each range block must be located in the same part of the image which the range blocks come from. The proposed fractal techniques were applied to face recognition using the MIT and XM2VTS face databases. Accuracies of 95% were obtained with up to 156 images.
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3

Heseltine, Thomas David. "Face recognition : two-dimensional and three-dimensional techniques." Thesis, University of York, 2005. http://etheses.whiterose.ac.uk/9880/.

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4

Sun, Yunlian <1986&gt. "Advanced Techniques for Face Recognition under Challenging Environments." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6355/1/sun_yunlian_tesi.pdf.

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Анотація:
Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries.
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5

Sun, Yunlian <1986&gt. "Advanced Techniques for Face Recognition under Challenging Environments." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6355/.

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Анотація:
Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries.
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6

Gul, Ahmet Bahtiyar. "Holistic Face Recognition By Dimension Reduction." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1056738/index.pdf.

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Анотація:
Face recognition is a popular research area where there are different approaches studied in the literature. In this thesis, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied in detail and three of the methods based on the Eigenface method are compared. These are the Bayesian PCA where Bayesian classifier is applied after dimension reduction with PCA, the Subspace Linear Discriminant Analysis (LDA) where LDA is applied after PCA and Eigenface where Nearest Mean Classifier applied after PCA. All the three methods are implemented on the Olivetti Research Laboratory (ORL) face database, the Face Recognition Technology (FERET) database and the CNN-TURK Speakers face database. The results are compared with respect to the effects of changes in illumination, pose and aging. Simulation results show that Subspace LDA and Bayesian PCA perform slightly well with respect to PCA under changes in pose
however, even Subspace LDA and Bayesian PCA do not perform well under changes in illumination and aging although they perform better than PCA.
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7

Mian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0046.

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Анотація:
[Truncated abstract] The aim of visual recognition is to identify objects in a scene and estimate their pose. Object recognition from 2D images is sensitive to illumination, pose, clutter and occlusions. Object recognition from range data on the other hand does not suffer from these limitations. An important paradigm of recognition is model-based whereby 3D models of objects are constructed offline and saved in a database, using a suitable representation. During online recognition, a similar representation of a scene is matched with the database for recognizing objects present in the scene . . . The tensor representation is extended to automatic and pose invariant 3D face recognition. As the face is a non-rigid object, expressions can significantly change its 3D shape. Therefore, the last part of this thesis investigates representations and matching techniques for automatic 3D face recognition which are robust to facial expressions. A number of novelties are proposed in this area along with their extensive experimental validation using the largest available 3D face database. These novelties include a region-based matching algorithm for 3D face recognition, a 2D and 3D multimodal hybrid face recognition algorithm, fully automatic 3D nose ridge detection, fully automatic normalization of 3D and 2D faces, a low cost rejection classifier based on a novel Spherical Face Representation, and finally, automatic segmentation of the expression insensitive regions of a face.
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8

Mian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0046.

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9

Al-Qatawneh, Sokyna M. S. "3D Facial Feature Extraction and Recognition. An investigation of 3D face recognition: correction and normalisation of the facial data, extraction of facial features and classification using machine learning techniques." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4876.

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Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods.
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10

Han, Xia. "Towards the Development of an Efficient Integrated 3D Face Recognition System. Enhanced Face Recognition Based on Techniques Relating to Curvature Analysis, Gender Classification and Facial Expressions." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5347.

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The purpose of this research was to enhance the methods towards the development of an efficient three dimensional face recognition system. More specifically, one of our aims was to investigate how the use of curvature of the diagonal profiles, extracted from 3D facial geometry models can help the neutral face recognition processes. Another aim was to use a gender classifier employed on 3D facial geometry in order to reduce the search space of the database on which facial recognition is performed. 3D facial geometry with facial expression possesses considerable challenges when it comes face recognition as identified by the communities involved in face recognition research. Thus, one aim of this study was to investigate the effects of the curvature-based method in face recognition under expression variations. Another aim was to develop techniques that can discriminate both expression-sensitive and expression-insensitive regions for ii face recognition based on non-neutral face geometry models. In the case of neutral face recognition, we developed a gender classification method using support vector machines based on the measurements of area and volume of selected regions of the face. This method reduced the search range of a database initially for a given image and hence reduces the computational time. Subsequently, in the characterisation of the face images, a minimum feature set of diagonal profiles, which we call T shape profiles, containing diacritic information were determined and extracted to characterise face models. We then used a method based on computing curvatures of selected facial regions to describe this feature set. In addition to the neutral face recognition, to solve the problem arising from data with facial expressions, initially, the curvature-based T shape profiles were employed and investigated for this purpose. For this purpose, the feature sets of the expression-invariant and expression-variant regions were determined respectively and described by geodesic distances and Euclidean distances. By using regression models the correlations between expressions and neutral feature sets were identified. This enabled us to discriminate expression-variant features and there was a gain in face recognition rate. The results of the study have indicated that our proposed curvature-based recognition, 3D gender classification of facial geometry and analysis of facial expressions, was capable of undertaking face recognition using a minimum set of features improving efficiency and computation.
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Книги з теми "FACE RECOGNITION TECHNIQUES"

1

1954-, Zhang Yu-Jin, ed. Advances in face image analysis: Techniques and technologies. Hershey, PA: Medical Information Science Reference, 2010.

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2

Jain, L. C., U. Halici, I. Hayashi, S. B. Lee, and S. Tsutsui. INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION. Boca Raton: Routledge, 2022. http://dx.doi.org/10.1201/9780203750520.

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3

M, Newton Elaine, and Information Technology Laboratory (National Institute of Standards and Technology). Mathematical and Computational Sciences Division., eds. Meta-analysis of face recognition algorithms. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, Mathematics and Computational Sciences Division, National Institute of Standards and Technology, 2001.

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4

Elaine, Newton, and Information Technology Laboratory (National Institute of Standards and Technology). Mathematical and Computational Sciences Division, eds. Meta-analysis of face recognition algorithms. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, Mathematics and Computational Sciences Division, National Institute of Standards and Technology, 2001.

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5

M, Newton Elaine, and Information Technology Laboratory (National Institute of Standards and Technology). Mathematical and Computational Sciences Division, eds. Meta-analysis of face recognition algorithms. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, Mathematics and Computational Sciences Division, National Institute of Standards and Technology, 2001.

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6

M, Newton Elaine, and Information Technology Laboratory (National Institute of Standards and Technology). Mathematical and Computational Sciences Division., eds. Meta-analysis of face recognition algorithms. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, Mathematics and Computational Sciences Division, National Institute of Standards and Technology, 2001.

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7

Yang, Ming-Hsuan. Face Detection and Gesture Recognition for Human-Computer Interaction. Boston, MA: Springer US, 2001.

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8

1950-, Ahuja Narendra, ed. Face detection and gesture recognition for human-computer interaction. Boston: Kluwer Academic, 2001.

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9

Wiskott, Laurenz. Labeled graphs and dynamic link matching for face recognition and scene analysis. Thun: Deutsch, 1995.

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10

International Conference on Automatic Face and Gesture Recognition (5th 2002 Washington, D.C.). Fifth IEEE International Conference on Automatic Face and Gesture Recognition: Proceedings : 20-21 May, 2002, Washington, D.C. Los Alamitos, Calif: IEEE Computer Society, 2002.

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Частини книг з теми "FACE RECOGNITION TECHNIQUES"

1

Zhang, Yu-Jin. "Face Recognition." In A Selection of Image Analysis Techniques, 253–96. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b23131-7.

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2

Tistarelli, Massimo, and Enrico Grosso. "Active Vision-based Face Recognition: Issues, Applications and Techniques." In Face Recognition, 262–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_14.

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3

Bamba, Inshita, Yashika, Jahanvi Singh, and Pronika Chawla. "Face Recognition Techniques and Implementation." In Emerging Technologies in Data Mining and Information Security, 345–56. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4052-1_35.

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4

Howell, A. Jonathan. "Introduction to Face Recognition." In INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION, 217–83. Boca Raton: Routledge, 2022. http://dx.doi.org/10.1201/9780203750520-7.

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5

Azmeen, Juhika, and Dibya Jyoti Borah. "Face Recognition Techniques, Challenges: A Review." In Soft Computing for Intelligent Systems, 369–74. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1048-6_27.

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6

Dharavath, K., F. A. Talukdar, R. H. Laskar, and N. Dey. "Face Recognition Under Dry and Wet Face Conditions." In Intelligent Techniques in Signal Processing for Multimedia Security, 253–71. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44790-2_12.

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7

Würtz, Rolf P. "Face Recognition from Correspondence Maps." In INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION, 335–53. Boca Raton: Routledge, 2022. http://dx.doi.org/10.1201/9780203750520-10.

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Pandya, A. S., and R. R. Szabo. "Neural Networks for Face Recognition." In INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION, 285–314. Boca Raton: Routledge, 2022. http://dx.doi.org/10.1201/9780203750520-8.

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Halici, U., L. C. Jain, and A. Erol. "Introduction to Fingerprint Recognition." In INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION, 1–34. Boca Raton: Routledge, 2022. http://dx.doi.org/10.1201/9780203750520-1.

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Deriche, Mohamed, and Mohammed Aleemuddin. "Pose-Invariant Face Recognition Using Subspace Techniques." In Computer-Aided Intelligent Recognition Techniques and Applications, 169–200. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470094168.ch11.

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Тези доповідей конференцій з теми "FACE RECOGNITION TECHNIQUES"

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Dai, Bo, Dengsheng Zhang, Hui Liu, Shixin Sun, and Ke Li. "Evaluation of face recognition techniques." In International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), edited by Honghua Tan and Qi Luo. SPIE, 2009. http://dx.doi.org/10.1117/12.836686.

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Sahu, Madhusmita, and Rasmita Dash. "Study on Face Recognition Techniques." In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182358.

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Prabhu Teja, G., and S. Ravi. "Face recognition using subspaces techniques." In 2012 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2012. http://dx.doi.org/10.1109/icrtit.2012.6206780.

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Giovani, Vanessa, Ivyna Johansen, Dea Asya Ashilla, and Novita Hanafiah. "Study on Face Recognition Techniques." In 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI). IEEE, 2021. http://dx.doi.org/10.1109/iccsai53272.2021.9609759.

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Hemathilaka, Susith, and Achala Aponso. "An Analysis of Face Recognition under Face Mask Occlusions." In 2nd International Conference on Machine Learning Techniques and Data Science (MLDS 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111804.

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Анотація:
The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.
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Boubeguira, Zeyneb, and Salim Ghanemi. "GPU-based Face Recognition Acceleration Techniques." In the Fourth International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3234698.3234744.

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Choudhary, Kritika, and Nidhi Goel. "A review on face recognition techniques." In International Conference on Communication and Electronics System Design, edited by Vijay Janyani, M. Salim, and K. K. Sharma. SPIE, 2013. http://dx.doi.org/10.1117/12.2012238.

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Alsamman, Abdul R., and Mohammad S. Alam. "Performance of invariant face recognition techniques." In International Symposium on Optical Science and Technology, edited by Bahram Javidi and Demetri Psaltis. SPIE, 2002. http://dx.doi.org/10.1117/12.450852.

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Hasan Alhafidh, Basman M., Rabee M. Hagem, and Amar I. Daood. "Face Detection and Recognition Techniques Analysis." In 2022 International Conference on Computer Science and Software Engineering (CSASE). IEEE, 2022. http://dx.doi.org/10.1109/csase51777.2022.9759573.

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Hasan Alhafidh, Basman M., Rabee M. Hagem, and Amar I. Daood. "Face Detection and Recognition Techniques Analysis." In 2022 International Conference on Computer Science and Software Engineering (CSASE). IEEE, 2022. http://dx.doi.org/10.1109/csase51777.2022.9759573.

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Звіти організацій з теми "FACE RECOGNITION TECHNIQUES"

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Kroll, Joshua A. ACM TechBrief: Facial Recognition Technology. ACM, February 2022. http://dx.doi.org/10.1145/3520137.

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Анотація:
Facial recognition is not a monolithic technology or a particular technique. Rather, facial recognition refers to any technology that automatically processes and purports to identify faces in images or videos. While humans interpret faces easily, computers must extract patterns from data or humans must code patterns into the system. Applying these patterns yields the facial descriptors (often referred to as faceprints) on which facial recognition systems rely to achieve their function.
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Meidan, Rina, and Robert Milvae. Regulation of Bovine Corpus Luteum Function. United States Department of Agriculture, March 1995. http://dx.doi.org/10.32747/1995.7604935.bard.

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The main goal of this research plan was to elucidate regulatory mechanisms controlling the development, function of the bovine corpus luteum (CL). The CL contains two different sterodigenic cell types and therefore it was necessary to obtain pure cell population. A system was developed in which granulosa and theca interna cells, isolated from a preovulatory follicle, acquired characteristics typical of large (LL) and small (SL) luteal cells, respectively, as judged by several biochemical and morphological criteria. Experiments were conducted to determine the effects of granulosa cells removal on subsequent CL function, the results obtained support the concept that granulosa cells make a substaintial contribution to the output of progesterone by the cyclic CL but may have a limited role in determining the functional lifespan of the CL. This experimental model was also used to better understand the contribution of follicular granulosa cells to subsequent luteal SCC mRNA expression. The mitochondrial cytochrome side-chain cleavage enzyme (SCC), which converts cholesterol to pregnenolone, is the first and rate-limiting enzyme of the steroidogenic pathway. Experiments were conducted to characterize the gene expression of P450scc in bovine CL. Levels of P450scc mRNA were higher during mid-luteal phase than in either the early or late luteal phases. PGF 2a injection decreased luteal P450scc mRNA in a time-dependent manner; levels were significantly reduced by 2h after treatment. CLs obtained from heifers on day 8 of the estrous cycle which had granulosa cells removed had a 45% reduction in the levels of mRNA for SCC enzymes as well as a 78% reduction in the numbers of LL cells. To characterize SCC expression in each steroidogenic cell type we utilized pure cell populations. Upon luteinization, LL expressed 2-3 fold higher amounts of both SCC enzymes mRNAs than SL. Moreover, eight days after stimulant removal, LL retained their P4 production capacity, expressed P450scc mRNA and contained this protein. In our attempts to establish the in vitro luteinization model, we had to select the prevulatory and pre-gonadotropin surge follicles. The ratio of estradiol:P4 which is often used was unreliable since P4 levels are high in atretic follicles and also in preovulatory post-gonadotropin follicles. We have therefore examined whether oxytocin (OT) levels in follicular fluids could enhance our ability to correctly and easily define follicular status. Based on E2 and OT concentrations in follicular fluids we could more accurately identify follicles that are preovulatory and post gonadotropin surge. Next we studied OT biosynthesis in granulosa cells, cells which were incubated with forskolin contained stores of the precursor indicating that forskolin (which mimics gonadotropin action) is an effective stimulator of OT biosynthesis and release. While studying in vitro luteinization, we noticed that IGF-I induced effects were not identical to those induced by insulin despite the fact that megadoses of insulin were used. This was the first indication that the cells may secrete IGF binding protein(s) which regonize IGFs and not insulin. In a detailed study involving several techniques, we characterized the species of IGF binding proteins secreted by luteal cells. The effects of exogenous polyunsaturated fatty acids and arachidonic acid on the production of P4 and prostanoids by dispersed bovine luteal cells was examined. The addition of eicosapentaenoic acid and arachidonic acid resulted in a dose-dependent reduction in basal and LH-stimulated biosynthesis of P4 and PGI2 and an increase in production of PGF 2a and 5-HETE production. Indomethacin, an inhibitor of arachidonic acid metabolism via the production of 5-HETE was unaffected. Results of these experiments suggest that the inhibitory effect of arachidonic acid on the biosynthesis of luteal P4 is due to either a direct action of arachidonic acid, or its conversion to 5-HETE via the lipoxgenase pathway of metabolism. The detailed and important information gained by the two labs elucidated the mode of action of factors crucially important to the function of the bovine CL. The data indicate that follicular granulosa cells make a major contribution to numbers of large luteal cells, OT and basal P4 production, as well as the content of cytochrome P450 scc. Granulosa-derived large luteal cells have distinct features: when luteinized, the cell no longer possesses LH receptors, its cAMP response is diminished yet P4 synthesis is sustained. This may imply that maintenance of P4 (even in the absence of a Luteotropic signal) during critical periods such as pregnancy recognition, is dependent on the proper luteinization and function of the large luteal cell.
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