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Auswahl der wissenschaftlichen Literatur zum Thema „Thermal face images“
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Zeitschriftenartikel zum Thema "Thermal face images"
Mostafa, Eslam, Riad Hammoud, Asem Ali und Aly Farag. „Face recognition in low resolution thermal images“. Computer Vision and Image Understanding 117, Nr. 12 (Dezember 2013): 1689–94. http://dx.doi.org/10.1016/j.cviu.2013.07.010.
Der volle Inhalt der QuelleAlbar, Albar, Hendrick Hendrick und Rahmad Hidayat. „Segmentation Method for Face Modelling in Thermal Images“. Knowledge Engineering and Data Science 3, Nr. 2 (31.12.2020): 99. http://dx.doi.org/10.17977/um018v3i22020p99-105.
Der volle Inhalt der QuelleQuan, Yan Ming, Hao Xu und Zhi Yong Ke. „Temperature Field Measurement of Turning Tool with Thermal Infrared Imager“. Advanced Materials Research 305 (Juli 2011): 265–68. http://dx.doi.org/10.4028/www.scientific.net/amr.305.265.
Der volle Inhalt der QuelleKanmani, Madheswari, und Venkateswaran Narasimhan. „Optimal fusion aided face recognition from visible and thermal face images“. Multimedia Tools and Applications 79, Nr. 25-26 (24.02.2020): 17859–83. http://dx.doi.org/10.1007/s11042-020-08628-9.
Der volle Inhalt der QuelleNagumo, Kent, Tomohiro Kobayashi, Kosuke Oiwa und Akio Nozawa. „Face Alignment in Thermal Infrared Images Using Cascaded Shape Regression“. International Journal of Environmental Research and Public Health 18, Nr. 4 (12.02.2021): 1776. http://dx.doi.org/10.3390/ijerph18041776.
Der volle Inhalt der QuelleChame, Kanchan P. „Face Recognition using Sketch, Thermal and Infrared Images“. International Journal for Research in Applied Science and Engineering Technology 9, Nr. 1 (31.01.2021): 178–90. http://dx.doi.org/10.22214/ijraset.2021.32751.
Der volle Inhalt der QuelleGrudzień, Artur, Marcin Kowalski und Norbert Pałka. „Thermal Face Verification through Identification“. Sensors 21, Nr. 9 (10.05.2021): 3301. http://dx.doi.org/10.3390/s21093301.
Der volle Inhalt der QuelleHermosilla, Gabriel, José Luis Verdugo, Gonzalo Farias, Esteban Vera, Francisco Pizarro und Margarita Machuca. „Face Recognition and Drunk Classification Using Infrared Face Images“. Journal of Sensors 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/5813514.
Der volle Inhalt der QuelleSancen-Plaza, Agustin, Luis M. Contreras-Medina, Alejandro Israel Barranco-Gutiérrez, Carlos Villaseñor-Mora, Juan J. Martínez-Nolasco und José A. Padilla-Medina. „Facial Recognition for Drunk People Using Thermal Imaging“. Mathematical Problems in Engineering 2020 (14.04.2020): 1–9. http://dx.doi.org/10.1155/2020/1024173.
Der volle Inhalt der QuelleFitriyah, Hurriyatul, Edita Rosana Widasari und Rekyan Regasari Mardi Putri. „Inner-Canthus Localization of Thermal Images in Face-View Invariant“. International Journal on Advanced Science, Engineering and Information Technology 8, Nr. 6 (04.12.2018): 2570. http://dx.doi.org/10.18517/ijaseit.8.5.3903.
Der volle Inhalt der QuelleDissertationen zum Thema "Thermal face images"
Guan, Lei. „Face Recognition with visible and thermal IR images“. Master's thesis, Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/96119.
Der volle Inhalt der QuelleM.S.E.
This thesis describes how the fusion of visible and thermal infrared (IR) images can be used to improve the performance of face recognition techniques, especially when illumination variations and occlusions are involved. Visible images are sensitive to illumination variations, while thermal IR images are robust to them. However, thermal IR images are degraded by occlusions caused from eyeglasses, but visible images can provide detailed information around the eyes even when eyeglasses are present. Fusion techniques, which combine complementary information from both spectrums, generate information that is robust to both illumination variations and occlusions. Before two images are fused, they must be registered. In this thesis, edge-based mutual information is used to register both visible and thermal IR images taken under different conditions. Following that, eyeglasses (if present) are removed from the thermal IR image, and replaced by eyes that are reconstructed from the visible image. Then, data-level, feature-level, and score-level fusion techniques are applied to the visible and thermal IR images for face recognition. Experimental results using the NIST/Equinox database showed that the fusion of visible and thermal IR images increased the number of first matches by 22% over visible images, and 8% over thermal IR images. Unfortunately, thermal IR sensors may be cost-prohibitive for many applications. In consideration of this, this thesis explores ways to predict a novelty component from the visible image. A novelty component is a thermal-like image that can be obtained from information in the visible image. It is later fused with the visible image for face recognition. Experimental results based upon four face recognition algorithms showed that the fusion of visible images and their novelty components increased the number of first matches over visible images by 21% (using the NIST/Equinox database) and 17% (using the Extended Yale Face Database B).
Temple University--Theses
Roman, Matej. „Automatizované měření teploty v boji proti COVID“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442439.
Der volle Inhalt der QuelleRibeiro, Ricardo Ferreira. „Face detection on infrared thermal image“. Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23551.
Der volle Inhalt der QuelleInfrared cameras or thermal imaging cameras are devices that use infrared radiation to capture an image. This kind of sensors are being developed for almost a century now. They started to be used in the military environment, but at that time it took too long to create a single image. Nowadays, the infrared sensors have reached a whole new technological level and are used for other than military purposes. These sensors are being used for face detection in this thesis. When comparing the use of thermal images regarding color images, it is possible to see advantages and limitations, such as capture images in total darkness and high price, respectively, which will be explored throughout this document. This work proposes the development or adaptation of several methods for face detection on infrared thermal images. The well known algorithm developed by Paul Viola and Michael Jones, using Haar feature-based cascade classi ers, is used to compare the traditional algorithms developed for visible light images when applied to thermal imaging. Three di erent algorithms for face detection are presented. Face segmentation is the rst step in these methods. A method for the segmentation and ltering of the face in the infrared thermal images resulting in a binary image is proposed. In the rst method, an edge detection algorithm is applied to the binary image and the face detection is based on these contours. In the second method, a template matching method is used for searching and nding the location of a template image with the shape of a human head in the binary image. In the last one, a matching algorithm is used. This algorithm correlates a template with the distance transform of the edge image. This algorithm incorporates edge orientation information resulting in the reduction of false detection and the cost variation is limited. The experimental results show that the proposed methods have promising outcome, but the second method is the most suitable for the performed experiments.
As camaras infravermelhas ou as camaras de imagem termica sao dispositivos que usam radiação infravermelha para capturar uma imagem. Este tipo de sensores estao a ser desenvolvidos há quase um século. Começaram a ser usados para fins militares, mas naquela época demorava demasiado tempo para criar uma única imagem. Hoje em dia, os sensores infravermelhos alcançaram um nível tecnológico totalmente novo e são usados para fins além de militares. Esses sensores estão ser usados para detecção facial nesta dissertação. Comparando o uso de imagens térmicas relativamente a imagens coloridas, é possível ver vantagens e limitações, tal como a captura de imagens na escuridão e o preço elevado, respectivamente, que serão exploradas durante este documento. Este trabalho propõe o desenvolvimento ou adaptação de vários métodos para a detecção facial em imagens térmicas. O conhecido algoritmo desenvolvido por Paul Viola e Michael Jones, que utiliza cascatas de classificadores de Haar baseado em características, é usado para comparar os algoritmos tradicionais desenvolvidos para imagens de luz visível quando aplicados a imagens térmicas. São apresentados três métodos diferentes para a detecção facial. A segmentação do rosto e o primeiro passo nestes métodos. E proposto um método para a segmentação e filtragem do rosto nas imagens térmicas que tem como resultado uma imagem binária. No primeiro método, é aplicado um algoritmo de detecção de contornos a imagem binária e a detecção facial é baseada nesses contornos. No segundo método, é usado um método de correspondência de padrões para pesquisar e encontrar a localização de uma imagem padrão com a forma da cabeça humana na imagem binária. No último, é usado um algoritmo de correspondência. Este algoritmo correlaciona um padrão com a transformada de distância da imagem de contornos. Este algoritmo incorpora informações de orientação de contornos que resulta na redução de falsas detecções e a variação do custo é limitada. Os resultados experimentais mostram que os métodos propostos têm resultados promissores, mas o segundo método é o mais adequado para as experiências realizadas.
Váňa, Jan. „Rozpoznávání termosnímků obličejů“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236689.
Der volle Inhalt der QuelleAit, Fqir Ali Fatima Zahra. „Développement et caractérisation de nouveaux procédés de passivation pour les capteurs d'images CMOS“. Thesis, Lyon 1, 2013. http://www.theses.fr/2013LYO10186.
Der volle Inhalt der QuelleIn order to maintain or enhance the electro-optical performances while decreasing the pixel size, advanced CMOS Image Sensors (CIS) requires the implementation of new architectures. For this purpose, deep trenches for pixel isolation (DTI) and backside illumination (BSI) have been introduced as ones of the most promising candidates. The major challenge of these architectures is the high dark current level (Idark) due to the generation/recombination centers present at both, DTI sidewalls and backside surfaces. Therefore, the creation of very shallow doped junctions at these surfaces reducing Idark and further crosstalk by drifting the photo-generated carriers to the photodiode region appears as key process step for introducing these architectures. For the backside surface passivation, a very shallow doped layer can be achieved by low-energy implantation followed by very short and localized heating provided by pulsed laser annealing (PLA). In the melt regime, box-shaped profiles with activation rates close to 100% and excellent crystalline quality have been achieved. The non-melt regime shows some potential, especially for multiple pulse conditions. In the optimal process conditions, very low level of Idark comparable to the standard reference has been achieved. In the other side, the passivation of DTI sidewalls has been performed by in-situ doped Epitaxy. Deposited layers with good uniformity and doping conformity all along the DTI cavity have been achieved. The electrical results show Idark values lower than the standard reference
Wang, Chi-Yao, und 王麒堯. „Face Recognition Based on Visible Images and Thermal Images“. Thesis, 2016. http://ndltd.ncl.edu.tw/handle/85479588320605025918.
Der volle Inhalt der Quelle國立中央大學
資訊工程學系
104
Nowadays, human identification is more and more important in security. The most important identification method is the use of the biometric feature. Either the commodities which recognize the authorized user with fingerprint or customs officers use the iris recognition system to identify passengers, they elaborate the convenient and the reliable of biometric identification. In the past, a lot of researches on fingerprint, voiceprint, palmprint, human face and iris. They use kinds of algorithms to find out stable feature which differs from person to person for identification. In our approach, we devise a method combine visible images with thermal images for identification. These two kinds of different images have pros and cons. They capture electromagnetic radiation in different ranges and show they include different information. Features extracted from visual images by classical method, Fisherface method. From thermal images, we get the temperature distribution, by physiology phenomenon, based on temperature gradient and morphology. We use the local square windows to count pixels of a net to make feature vectors indicating images, which is called counter filter. Finally, we use two feature vectors and turn them into longer vectors, and then classify them with KNN classifier. Experimental results demonstrate that the performance of the system with multi-model is better than one with a single model.
Chuang, Yi-Jui, und 莊宜叡. „A Comparative Analysis of Thermal Infrared Face Images Feature Extractions and Classifiers“. Thesis, 2009. http://ndltd.ncl.edu.tw/handle/18417423371242232165.
Der volle Inhalt der Quelle中原大學
機械工程研究所
97
Fully automatic face and expression recognition systems have received increasingly attention in recently years. However, the classification performance in the visible light face recognition system is often directly affected by the light source changed. Therefore, the visible light face recognition must control the factors of the light source to get the better results. This thesis used the previous feature extraction methods (Principal Component Analysis, Linear Discriminant Analysis, and Generalized Discriminant Analysis) and joined the Kernel Principal Component Analysis to apply to the thermal face images to make the comprehensive comparison. In this thesis, we joined the Support Vector Machine (SVM) classifier and the previous classifiers (Nearest Mean Classifier and K Nearest Neighbor Classifier) for the comparison. Comparing the SVM classifier with the others ,the classification performance using it in the thermal imagery face recognition is better than the others, and the result also establishes that directly using the gray-level values of the thermal images to classify can get higher classification rates. This thesis establishes the thermal infrared face image database of Chung Yuan Christian University which includes 50 individuals, each person 60 thermal images, totally 3000 images. The results show that using the images containing face contours using PCA, KPCA, LDA and GDA respectively and using the SVM classifier can achieve 100% of the classification rate. For the images do not contain the face contours, the classified rate using the SVM classifier with GDA is 99.2 percent, and with KPCA can reach as higher as 100%.
Shih, Wei-Tsun, und 施惟尊. „Dynamically Adjust Fusion Proportion Of Multiple Visible and Thermal Images For Robust Face Recognition“. Thesis, 2009. http://ndltd.ncl.edu.tw/handle/05520768059294396625.
Der volle Inhalt der Quelle國立成功大學
電腦與通信工程研究所
97
Because people have paid more and more attention on the issue of personal safety in recent years, as a result, there are many identification mechanisms produced rapidly. Compared to the fingerprint recognition system, as a result of face recognition system for people without physical touch, it is more convenient to use and thus become an important identification mechanism. However, for the face recognition systems which only use visible images, it’s recognition rate greatly affected by environmental illumination, and vulnerable to other people trying to fool the system by the photographs and made an error recognition. Use the fusion images fused of visible images and thermal images taken at the same time to perform face recognition can reduce the above-mentioned drawbacks of the face recognition system which only use visible images. In this paper, we use DWT coefficients to fuse visible and thermal images, and propose a fusion method which dynamically adjust the fusion proportion of visible and thermal images. This method can dynamically adjust the fusion proportion of visible and thermal images depends on the difference of illumination of visible images. Then we use multiple fusion images of every person to perform LDA to transform character parameters of fusion images. Finally, the use of Euclidean distance to the vesting of face recognition. The use of multiple images of every person and the fusion method proposed in this paper, thus can reduce the influence of environmental illumination to enhance the security and recognition rate. The experimental results show that non-uniform illumination in the environment, the recognition rate by using the fusion method proposed in this paper to adjust dynamically the fusion proportion of visible and thermal images can be better than that by using the fusion method proposed previously to fuse visible and thermal images when both using multiple fusion images of every person. Prove that the use of the fusion method proposed in this paper can further reduce the influence of environmental illumination to enhance the security and recognition rate.
Jhang, You-Ruei, und 張佑瑞. „Face Recognition Based on Thermal Image Feature“. Thesis, 2017. http://ndltd.ncl.edu.tw/handle/796tp9.
Der volle Inhalt der Quelle國立臺北科技大學
電子工程系研究所
105
With technological advances, people pay more and more attention on personal security issues in recent years, the traditional method of identification is gradually replaced by new technology, in which facial recognition is the most popular technique, however, the recognition rate of face recognition system using visible image is affected of environmental illumination, and it easily to take cheat to the system by the photographs, the above problems result in reducing the security. Therefore, scholars propose replacing the visible image with thermal images to solve foregoing problems, but the effective method of thermal face positioning has not yet completed, thus the identification is limited to particular environment or immovable location. In this thesis, we design a real-time and dynamic method in recognition of human face, in which the distribution of facial temperature as personal features by the adaptive binarization algorithm, which can not only detect facial position accurately, but also capture appropriate size of face, finally, we use of Back Propagation Neural Network (BP) to identify face. The experiment shows that our system can detect position of face dynamically in complex environment, the recognition rate can reach 96.07%, we enhance feasibility of facial recognition system using thermal image in the reality level as our contribution.
Lin, Hong-Kai, und 林宏鎧. „An Identity Recognition System using Integration Face Thermal Image and Human Laser Scanned Features“. Thesis, 2012. http://ndltd.ncl.edu.tw/handle/04914068065195502493.
Der volle Inhalt der Quelle國立臺灣師範大學
工業教育學系
101
Nowadays, the development of biometric based identity recognition technology is getting faster, causing the industries to make transformation; especially the access management system, which are using fingerprint, voice, and iris recognition equipment to replace humans. As a result, how to make these identity recognition systems work efficiently and accurately, becomes the critical point. In this research, the researcher proposed an identity recognition system based on the fusion of thermal image and laser range image, and establish a human body feature database. The system is divided into two stages, which are the data build stage and recognition stage, as detailed below. In the part of thermal face feature database building stage, we use half-circle face segmentation and temperature layer splitter algorithm to do the feature extraction task. In the part of the human body feature database building stage, we use the human body range image and the trigonometric functions to calculate the height of the human body and the forehead as the basis of the identity recognition task. This system in recognition stage, we use the data of the face temperature and the body shape feature stored in the feature database, to calculate the error rate of the body shape and the chi-square value of the face temperature with the measured value, and use the expert system to do the recognition task. In the last part, we introduce a performance indicator and use 33 person experiment to get the 96.9% recognition rate.
Buchteile zum Thema "Thermal face images"
Silva, Gustavo, Rui Monteiro, André Ferreira, Pedro Carvalho und Luís Corte-Real. „Face Detection in Thermal Images with YOLOv3“. In Advances in Visual Computing, 89–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33723-0_8.
Der volle Inhalt der QuelleFaundez-Zanuy, Marcos, Xavier Font-Aragones und Jiri Mekyska. „Preliminary Experiments on Thermal Emissivity Adjustment for Face Images“. In Progresses in Artificial Intelligence and Neural Systems, 155–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5093-5_15.
Der volle Inhalt der QuellePlasencia, Yenisel, Edel García-Reyes, Robert P. W. Duin, Heydi Mendez-Vazquez, César San-Martin und Claudio Soto. „A Study on Representations for Face Recognition from Thermal Images“. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 185–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_22.
Der volle Inhalt der QuelleFloody, Ramiro Donoso, César San Martín und Heydi Méndez-Vázquez. „Face Recognition Using TOF, LBP and SVM in Thermal Infrared Images“. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 683–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_81.
Der volle Inhalt der QuelleSeal, Ayan, Debotosh Bhattacharjee, Mita Nasipuri und Dipak Kumar Basu. „Minutiae from Bit-Plane Sliced Thermal Images for Human Face Recognition“. In Advances in Intelligent and Soft Computing, 113–24. New Delhi: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0491-6_11.
Der volle Inhalt der QuelleMian, Ajmal. „Comparison of Visible, Thermal Infra-Red and Range Images for Face Recognition“. In Advances in Image and Video Technology, 807–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92957-4_70.
Der volle Inhalt der QuelleForczmański, Paweł. „Human Face Detection in Thermal Images Using an Ensemble of Cascading Classifiers“. In Hard and Soft Computing for Artificial Intelligence, Multimedia and Security, 205–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48429-7_19.
Der volle Inhalt der QuelleAgarwal, Sumit, Harshit S. Sikchi, Suparna Rooj, Shubhobrata Bhattacharya und Aurobinda Routray. „Illumination-Invariant Face Recognition by Fusing Thermal and Visual Images via Gradient Transfer“. In Advances in Intelligent Systems and Computing, 658–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_48.
Der volle Inhalt der QuelleKopaczka, Marcin, Jan Nestler und Dorit Merhof. „Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches“. In Advanced Concepts for Intelligent Vision Systems, 518–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70353-4_44.
Der volle Inhalt der QuelleChoraś, Ryszard S. „Thermal Face Recognition“. In Image Processing and Communications Challenges 7, 37–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23814-2_5.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Thermal face images"
Andonova, A., und A. Radev. „Method of face recognition from thermal images“. In 2012 Quantitative InfraRed Thermography. QIRT Council, 2012. http://dx.doi.org/10.21611/qirt.2012.350.
Der volle Inhalt der QuelleMekyska, Jiri, Virginia Espinosa-Duro und Marcos Faundez-Zanuy. „Face segmentation: A comparison between visible and thermal images“. In 2010 IEEE International Carnahan Conference on Security Technology (ICCST). IEEE, 2010. http://dx.doi.org/10.1109/ccst.2010.5678709.
Der volle Inhalt der QuelleHussien, M. Naeem, Mohd-Haris Lye, Mohammad Faizal Ahmad Fauzi, Tan Ching Seong und Sarina Mansor. „Comparative analysis of eyes detection on face thermal images“. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2017. http://dx.doi.org/10.1109/icsipa.2017.8120641.
Der volle Inhalt der QuelleBauer, Joanna, Halina Podbielska, Artur Suchwalko und Jacek Mazurkiewicz. „Optical correlators for recognition of human face thermal images“. In Congress on Optics and Optoelectronics, herausgegeben von Zbigniew Jaroszewicz, Sergei Y. Popov und Frank Wyrowski. SPIE, 2005. http://dx.doi.org/10.1117/12.624128.
Der volle Inhalt der QuelleRiggan, Benjamin S., Nathaniel J. Short und Shuowen Hu. „Thermal to Visible Synthesis of Face Images Using Multiple Regions“. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00010.
Der volle Inhalt der QuelleIlikci, Burak, Lei Chen, Hyuk Cho und Qingzhong Liu. „Heat-Map Based Emotion and Face Recognition from Thermal Images“. In 2019 Computing, Communications and IoT Applications (ComComAp). IEEE, 2019. http://dx.doi.org/10.1109/comcomap46287.2019.9018786.
Der volle Inhalt der QuelleAL-Khalidi, Farah Q., Reza Saatchi, Derek Burke und Heather Elphick. „Tracking human face features in thermal images for respiration monitoring“. In 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2010. http://dx.doi.org/10.1109/aiccsa.2010.5586994.
Der volle Inhalt der QuelleMenon, Sooraj, Swathi J., Anit S.K., Anu P. Nair und Sarath S. „Driver Face Recognition and Sober Drunk Classification using Thermal Images“. In 2019 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2019. http://dx.doi.org/10.1109/iccsp.2019.8697908.
Der volle Inhalt der QuelleBhowmik, Mrinal Kanti, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu und Mahantapas Kundu. „Optimum fusion of visual and thermal face images for recognition“. In 2010 Sixth International Conference on Information Assurance and Security (IAS). IEEE, 2010. http://dx.doi.org/10.1109/isias.2010.5604191.
Der volle Inhalt der QuelleMoon, Sangwoo, Seong Kong, Jang-hee Yoo und Kyoil Chung. „Face Recognition with Multiscale Data Fusion of Visible and Thermal Images“. In 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety. IEEE, 2006. http://dx.doi.org/10.1109/cihsps.2006.313295.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Thermal face images"
Socolinsky, Diego A., Lawrence B. Wolff, Joshua D. Neuheisel und Christopher K. Eveland. Illumination Invariant Face Recognition Using Thermal Infrared Imagery. Fort Belvoir, VA: Defense Technical Information Center, Januar 2006. http://dx.doi.org/10.21236/ada444367.
Der volle Inhalt der QuelleSocolinsky, Diego A., und Andrea Selinger. A Comparative Analysis of Face Recognition Performance With Visible and Thermal Infrared Imagery. Fort Belvoir, VA: Defense Technical Information Center, Januar 2002. http://dx.doi.org/10.21236/ada453159.
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