Relevant bibliographies by topics / Low Resolution Face Recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Low Resolution Face Recognition'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Low Resolution Face Recognition"

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Chen, Jin, Jun Chen, Zheng Wang, Chao Liang, and Chia-Wen Lin. "Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition." IEEE Signal Processing Letters 27 (2020): 645–49. http://dx.doi.org/10.1109/lsp.2020.2986942.

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Wang, Zhifei, Zhenjiang Miao, Q. M. Jonathan Wu, Yanli Wan, and Zhen Tang. "Low-resolution face recognition: a review." Visual Computer 30, no. 4 (2013): 359–86. http://dx.doi.org/10.1007/s00371-013-0861-x.

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Zou, W. W. W., and P. C. Yuen. "Very Low Resolution Face Recognition Problem." IEEE Transactions on Image Processing 21, no. 1 (2012): 327–40. http://dx.doi.org/10.1109/tip.2011.2162423.

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Hong, Sungeun, and Jongbin Ryu. "Unsupervised Face Domain Transfer for Low-Resolution Face Recognition." IEEE Signal Processing Letters 27 (2020): 156–60. http://dx.doi.org/10.1109/lsp.2019.2963001.

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Xu, Xiang, Wanquan Liu, and Ling Li. "Low Resolution Face Recognition in Surveillance Systems." Journal of Computer and Communications 02, no. 02 (2014): 70–77. http://dx.doi.org/10.4236/jcc.2014.22013.

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Mostafa, Eslam, Riad Hammoud, Asem Ali, and Aly Farag. "Face recognition in low resolution thermal images." Computer Vision and Image Understanding 117, no. 12 (2013): 1689–94. http://dx.doi.org/10.1016/j.cviu.2013.07.010.

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Han, Feng, Xudong Wang, Furao Shen, and Jian Zhao. "C-Face: Using Compare Face on Face Hallucination for Low-Resolution Face Recognition." Journal of Artificial Intelligence Research 74 (August 16, 2022): 1715–37. http://dx.doi.org/10.1613/jair.1.13816.

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Face hallucination is a task of generating high-resolution (HR) face images from low-resolution (LR) inputs, which is a subfield of the general image super-resolution. However, most of the previous methods only consider the visual effect, ignoring how to maintain the identity of the face. In this work, we propose a novel face hallucination model, called C-Face network, which can generate HR images with high visual quality while preserving the identity information. A face recognition network is used to extract the identity features in the training process. In order to make the reconstructed face images keep the identity information to a great extent, a novel metric, i.e., C-Face loss, is proposed. We also propose a new training algorithm to deal with the convergence problem. Moreover, since our work mainly focuses on the recognition accuracy of the output, we integrate face recognition into the face hallucination process which ensures that the model can be used in real scenarios. Extensive experiments on two large scale face datasets demonstrate that our C-Face network has the best performance compared with other state-of-the-art methods.
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Li, Sisi, Zhonghua Liu, Di Wu, Hua Huo, Haijun Wang, and Kaibing Zhang. "Low-resolution face recognition based on feature-mapping face hallucination." Computers and Electrical Engineering 101 (July 2022): 108136. http://dx.doi.org/10.1016/j.compeleceng.2022.108136.

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Peng, Yuxi, Luuk Spreeuwers, and Raymond Veldhuis. "Low‐resolution face alignment and recognition using mixed‐resolution classifiers." IET Biometrics 6, no. 6 (2017): 418–28. http://dx.doi.org/10.1049/iet-bmt.2016.0026.

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Maity, Sayan, Mohamed Abdel-Mottaleb, and Shihab S. Asfour. "Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video." Electronics 10, no. 9 (2021): 1013. http://dx.doi.org/10.3390/electronics10091013.

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Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.
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Dissertations / Theses on the topic "Low Resolution Face Recognition"

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Arachchige, Somi Ruwan Budhagoda. "Face recognition in low resolution video sequences using super resolution /." Online version of thesis, 2008. http://hdl.handle.net/1850/7770.

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Roeder, James Roger. "Assessment of super-resolution for face recognition from very-low resolution images." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Kramer, Annika. "Model based methods for locating, enhancing and recognising low resolution objects in video." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/585.

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Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object detection, tracking and object recognition.Therefore, the aim of this thesis is to detect, enhance and recognise objects by incorporating a priori information and by using model based approaches. Motivated by the increasing demand for automatic methods for object detection, enhancement and recognition in video surveillance, different aspects of the video processing task are investigated with a focus on human faces. In particular, the challenge of fully automatic face pose and shape estimation by fitting a deformable 3D generic face model under varying pose and lighting conditions is tackled. Principal Component Analysis (PCA) is utilised to build an appearance model that is then used within a particle filter based approach to fit the 3D face mask to the image. This recovers face pose and person-specific shape information simultaneously. Experiments demonstrate the use in different resolution and under varying pose and lighting conditions. Following that, a combined tracking and super resolution approach enhances the quality of poor detail video objects. A 3D object mask is subdivided such that every mask triangle is smaller than a pixel when projected into the image and then used for model based tracking. The mask subdivision then allows for super resolution of the object by combining several video frames. This approach achieves better results than traditional super resolution methods without the use of interpolation or deblurring.Lastly, object recognition is performed in two different ways. The first recognition method is applied to characters and used for license plate recognition. A novel character model is proposed to create different appearances which are then matched with the image of unknown characters for recognition. This allows for simultaneous character segmentation and recognition and high recognition rates are achieved for low resolution characters down to only five pixels in size. While this approach is only feasible for objects with a limited number of different appearances, like characters, the second recognition method is applicable to any object, including human faces. Therefore, a generic 3D face model is automatically fitted to an image of a human face and recognition is performed on a mask level rather than image level. This approach does not require an initial pose estimation nor the selection of feature points, the face alignment is provided implicitly by the mask fitting process.
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SILVA, José Ivson Soares da. "Reconhecimento facial em imagens de baixa resolução." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16367.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-04-07T12:14:52Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dissertação_jiss_ciênciadacomputação.pdf: 2819671 bytes, checksum: 98f583c2b7105c3a5b369b2b48097633 (MD5)<br>Made available in DSpace on 2016-04-07T12:14:52Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dissertação_jiss_ciênciadacomputação.pdf: 2819671 bytes, checksum: 98f583c2b7105c3a5b369b2b48097633 (MD5) Previous issue date: 2015-02-24<br>FADE<br>Tem crescido o uso de sistemas computacionais para reconhecimento de pessoas por meio de dados biométricos, consequentemente os métodos para realizar o reconhecimento tem evoluído. A biometria usada no reconhecimento pode ser face, voz, impressão digital ou qualquer característica física capaz de distinguir as pessoas. Mudanças causadas por cirurgias, envelhecimento ou cicatrizes, podem não causar mudanças significativas nas características faciais tornando possível o reconhecimento após essas mudanças de aparência propositais ou não. Por outro lado tais mudanças se tornam um desafio para sistemas de reconhecimento automático. Além das mudanças físicas há outros fatores na obtenção da imagem que influenciam o reconhecimento facial como resolução da imagem, posição da face em relação a câmera, iluminação do ambiente, oclusão, expressão. A distância que uma pessoa aparece na cena modifica a resolução da região da sua face, o objetivo de sistemas direcionados a esse contexto é que a influência da resolução nas taxas de reconhecimento seja minimizada. Uma pessoa mais distante da câmera tem sua face na imagem numa resolução menor que uma que esteja mais próxima. Sistemas de reconhecimento facial têm um menor desempenho ao tratar imagens faciais de baixa resolução. Uma das fases de um sistema de reconhecimento é a extração de características, que processa os dados de entrada e fornece um conjunto de informações mais representativas das imagens. Na fase de extração de características os padrões da base de dados de treinamento são recebidos numa mesma dimensão, ou seja, no caso de imagens numa mesma resolução. Caso as imagens disponíveis para o treinamento sejam de resoluções diferentes ou as imagens de teste sejam de resolução diferente do treinamento, faz-se necessário que na fase de pré-processamento haja um tratamento de resolução. O tratamento na resolução pode ser aplicando um aumento da resolução das imagens menores ou redução da resolução das imagens maiores. O aumento da resolução não garante um ganho de informação que possa melhorar o desempenho dos sistemas. Neste trabalho são desenvolvidos dois métodos executados na fase de extração de características realizada por Eigenface, os vetores de características são redimensionados para uma nova escala menor por meio de interpolação, semelhante ao que acontece no redimensionamento de imagens. No primeiro método, após a extração de características, os vetores de características e as imagens de treinamento são redimensionados. Então, as imagens de treinamento e teste são projetadas no espaço de características pelos vetores de dimensão reduzida. No segundo método, apenas os vetores de características são redimensionados e multiplicados por um fator de compensação. Então, as imagens de treinamento são projetadas pelos vetores originais e as imagens de teste são projetadas pelos vetores reduzidos para o mesmo espaço. Os métodos propostos foram testados em 4 bases de dados de reconhecimento facial com a presença de problemas de variação de iluminação, variação de expressão facial, presença óculos e posicionamento do rosto.<br>In the last decades the use of computational systems to recognize people by biometric data is increasing, consequently the efficacy of methods to perform recognition is improving. The biometry used for recognition can be face, voice, fingerprint or other physical feature that enables the distiction of different persons. Facial changes caused by surgery, aging or scars, does not necessarily causes significant changes in facial features. For a human it is possible recognize other person after these interventions of the appearance. On the other hand, these interventions become a challenge to computer recognition systems. Beyond the physical changes there are other factors in aquisition of an image that influence the face recognition such as the image resolution, position between face and camera, light from environment, occlusions and variation of facial expression. The distance that a person is at image aquisition changes the resolution of face image. The objective of systems for this context is to minimize the influence of the image resolution for the recognition. A person more distant from the camera has the image of the face in a smaller resolution than a person near the camera. Face recognition systems have a poor performance to analyse low resolution image. One of steps of a recognition system is the features extraction that processes the input data so provides more representative images. In the features extraction step the images from the training database are received at same dimension, in other words, to analyse the images they have the same resolution. If the training images have different resolutions of test images it is necessary a preprocessing to normalize the image resolution. The preprocessing of an image can be to increase the resolution of small images or to reduce the resolution of big images. The increase resolution does not guarantee that there is a information gain that can improves the performance of the recognition systems. In this work two methods are developed at features extraction step based on Eigenface. The feature vectors are resized to a smaller scale, similar to image resize. In first method, after the feature extraction step, the feature vectors and the training images are resized. Then the training and test images are projected to feature space by the resized feature vectors. In second method, only the feature vectors are resized and multiplied by a compensation factor. The training images are projected by original feature vectors and the test images are projected by resized feature vectors to the same space. The proposed methods were tested in 4 databases of face recognition with presence of light variation, variation of facial expression, use of glasses and face position.
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Prado, Kelvin Salton do. "Comparação de técnicas de reconhecimento facial para identificação de presença em um ambiente real e semicontrolado." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-07012018-222531/.

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O reconhecimento facial é uma tarefa que os seres humanos realizam naturalmente todos os dias e praticamente sem esforço nenhum. Porém para uma máquina este processo não é tão simples. Com o aumento do poder computacional das máquinas atuais criou-se um grande interesse no processamento de imagens e vídeos digitais, com aplicações nas mais diversas áreas de conhecimento. Este trabalho objetiva a comparação de técnicas de reconhecimento facial, já conhecidas na literatura, com o intuito de identificar qual técnica possui melhor desempenho em um ambiente real e semicontrolado. Secundariamente avalia-se a possibilidade da utilização de uma ou mais técnicas de reconhecimento facial para identificar automaticamente a presença de alunos em uma sala de aula de artes marciais, utilizando imagens das câmeras de vigilância instaladas no recinto, levando em consideração aspectos importantes, tais como: imagens com pouca nitidez, luminosidade não ideal, movimentação constante dos alunos e o fato das câmeras estarem em um ângulo fixo. Este trabalho está relacionado às áreas de Processamento de Imagens e Reconhecimento de Padrões, e integra a linha de pesquisa de \"Monitoramento de Presença\" do projeto \"Ensino e Monitoramento de Atividades Físicas via Técnicas de Inteligência Artificial\" (Processo 2014.1.923.86.4, publicado no DOE 125(45), em 10/03/2015), projeto este executado em conjunto da Universidade de São Paulo, Faculdade Campo Limpo Paulista e Academia Central Kungfu-Wushu. Com os experimentos realizados e apresentados neste trabalho foi possível concluir que, dentre os métodos de reconhecimento facial utilizados, o método Local Binary Patterns teve o melhor desempenho no ambiente proposto. Por outro lado, o método Eigenfaces teve o pior desempenho de acordo com os experimentos realizados. Além disso, foi possível concluir também que não é viável a realização da detecção de presença automática de forma confiável no ambiente proposto, pois a taxa de reconhecimento facial foi relativamente baixa, se comparada a outros trabalhos do estado da arte, trabalhos estes que usam de ambientes de testes mais amigáveis, mas ao mesmo tempo menos comumente encontrados em nosso dia-a-dia. Acredita-se que foi possível alcançar os objetivos propostos pelo trabalho e que o mesmo possa contribuir para o estado da arte atual na área de visão computacional, mais precisamente no âmbito do reconhecimento facial. Ao final são sugeridos alguns trabalhos futuros que podem ser utilizados como ponto de partida para a continuação desta pesquisa ou até mesmo de novas pesquisas relacionadas a este tema<br>Face recognition is a task that human beings perform naturally in their everyday lives, usually with no effort at all. To machines, however, this process is not so simple. With the increasing computational power of current machines, a great interest was created in the field of digital videos and images processing, with applications in most diverse areas of knowledge. This work aims to compare face recognition techniques already know in the literature, in order to identify which technique has the best performance in a real and semicontrolled environment. As a secondary objective, we evaluate the possibility of using one or more face recognition techniques to automatically identify the presence of students in a martial arts classroom using images from the surveillance cameras installed in the room, taking into account important aspects such as images with low sharpness, illumination variation, constant movement of students and the fact that the cameras are at a fixed angle. This work is related to the Image Processing and Pattern Recognition areas, and integrates the research line \"Presence Monitoring\" of the project entitled \"Education and Monitoring of Physical Activities using Artificial Intelligence Techniques\" (Process 2014.1.923.86.4, published in DOE 125 (45) on 03/10/2015), developed as a partnership between the University of São Paulo, Campo Limpo Paulista Faculty, and Kungfu-Wushu Central Academy. With the experiments performed and presented in this work it was possible to conclude that, amongst all face recognition methods that were tested, Local Binary Patterns had the best performance in the proposed environment. On the other hand, Eigenfaces had the worse performance according to the experiments. Moreover, it was also possible to conclude that it is not feasible to perform the automatic presence detection reliably in the proposed environment, since the face recognition rate was relatively low, compared to the state of the art which uses, in general, more friendly test environments but at the same time less likely found in our daily lives. We believe that it was possible to achieve the objectives proposed by this work and that can contribute to the current state of the art in the computer vision field and, more precisely, in the face recognition area. Finally, some future work is suggested that can be used as a starting point for the continuation of this work or even for new researches related to this topic
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Bilson, Amy Jo. "Image size and resolution in face recognition /." Thesis, Connect to this title online; UW restricted, 1987. http://hdl.handle.net/1773/9166.

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Lin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/16703/1/Frank_Lin_Thesis.pdf.

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Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.
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Lin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16703/.

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Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.
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Naim, Mamoun. "New techniques in the recognition of very low resolution images." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266343.

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Li, Kai Chee. "Object identification from a low resolution laser radar system." Thesis, University of Surrey, 1992. http://epubs.surrey.ac.uk/844536/.

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Range is a very important and useful physical property. We can extract most of the physical features of an object from a 3-D image. This thesis is about analysing range images taken from a low resolution laser radar system. The objective of this research is to locate and attempt to identify obstacles in the surroundings for an unmanned small tracked vehicle to find its way. A short range (less than 30 metres) laser radar range finder, provided by the Ministry of Defense, gathered range images around the vehicle. Trees, rocks and walls are classified as obstacles. Roads, grassland and bushes are classified as passable objects. In the cases where the objects cannot be identified, we use the steepness as a guideline to classify the object as obstacles or not. Simple image processing techniques are applied to analyse the range image and satisfactory results are obtained. Obstacles can be located in the range images. The images are first segmented by three methods. Firstly, the range gating method is applied which segments the images- according to the information in their range histograms. Secondly, the gradient thresholding method is applied which distinguishes the steep obstacles from the non-steep objects. Thirdly, the spatial isolation is applied which isolates each individual object. The only information contained in a range image is the three dimensions of the object, so we concentrated on the analysis of the physical properties. Besides the size and shape, the texture of an object can also be extracted. Texture reflects what type of objects we are looking at. Walls, plains and other flat objects have fine textures while trees and bushes have rough textures. We have investigated various textural properties derived from the co-occurrence matrix. Another important physical property is the gradient because high gradient always implies obstacles, and these are things which an un-manned vehicle must avoid. The classification method uses the distance function to classify objects. Finally, the algorithm is implemented on an array of transputers. Promising results were observed. By implementing the algorithm onto an array of transputers, the processing time was reduced, and the obstacles can be identified from the range images.
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Books on the topic "Low Resolution Face Recognition"

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Keitel, Stefan. Development of a microprocessor based low cost, low resolution image recognition system. Brunel University, 1987.

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Video-to-Video Face Recognition for Low-Quality Surveillance Data. KIT Scientific Publishing, 2018.

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Antonios, Tzanakopoulos. Part I The International Law of Tainted Money, 5 International Legal Sources IV—the European Union and the Council of Europe. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198716587.003.0005.

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This chapter examines the European Union (EU) from the perspective of public international law. Financial crime, this chapter argues, is an international phenomenon that requires international solutions. This recognition has resulted in states cooperating on a number of different levels in order to streamline their responses to international financial crime. This chapter deals with the EU in two guises: as an independent source of international obligation for its member states, imposing discreet international obligations on them, which may even function so as to turn non-binding recommendations of international bodies into hard law, and as a peculiar entity interposing itself between international obligations from other sources (such as international treaties or UN Security Council binding resolutions) and domestic implementation of these obligations. The fact that some EU law has a more remote international source (such as a UN Security Council Resolution) may affect the degree and nature of the judicial scrutiny to which this body of law may be subject. The chapter looks first at sanctions and next at money laundering, both from the perspective of the EU.
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Alex, Mills. Part X Judicial Review, Judicial Performance, and Enforcement, 31 The Principled English Ambivalence to Law and Dispute Resolution Beyond the State. Oxford University Press, 2016. http://dx.doi.org/10.1093/law/9780198783206.003.0032.

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This chapter examines what appears to be an ambivalence in English law towards non-state forms of law and dispute resolution. It begins by focusing on the fact that an English court will not recognize the validity of a choice of non-state law in a contract, but will nevertheless recognize and enforce an arbitral award based on the application of non-state law, identically chosen by the parties. It then deals with the English courts’ attitude to the recognition and enforcement of a foreign arbitral award which has been set aside by the courts of the seat of the arbitration, under which the arbitral award is neither voided, nor necessarily still enforceable. In both of the contexts examined in this chapter-the question of the validity of a choice of non-state law, and the question of the enforceability of an arbitral award set aside by the courts of the arbitral seat-the underlying issue is the extent to which English law and courts are receptive to non-state norms and normative processes, and the extent to which they remain in a paradigm under which states are the exclusive sovereign actors. In both contexts, the English legal system strikes a balance.
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Buhlmann, Ulrike, and Andrea S. Hartmann. Cognitive and Emotional Processing in Body Dysmorphic Disorder. Edited by Katharine A. Phillips. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190254131.003.0022.

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According to current cognitive-behavioral models, body dysmorphic disorder (BDD) is characterized by a vicious cycle between maladaptive appearance-related thoughts and information-processing biases, as well as maladaptive behaviors and negative emotions such as feelings of shame, disgust, anxiety, and depression. This chapter provides an overview of findings on cognitive characteristics such as dysfunctional beliefs, information-processing biases for threat (e.g., selective attention, interpretation), and implicit associations (e.g., low self-esteem, strong physical attractiveness stereotype, and high importance of attractiveness). The chapter also reviews face recognition abnormalities and emotion recognition deficits and biases (e.g., misinterpreting neutral faces as angry) as well as facial discrimination ability. These studies suggest that BDD is associated with dysfunctional beliefs about one’s own appearance, information-processing biases, emotion recognition deficits and biases, and selective processing of appearance-related information. Future steps to stimulate more research and clinical implications are discussed.
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Martin, Graham R. The Sensory Ecology of Collisions and Entrapment. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199694532.003.0009.

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Collisions of birds with human artefacts (power lines, wind turbines, glass sheets, etc.) are major source of bird mortality. Many birds are also killed by entrapment in fishing nets. A sensory ecology perspective on this problem shows that collision and entrapment occur because these hazards present perceptual tasks that are beyond the capacities of the birds; birds are carrying out tasks where a hazard would not be predicted; or birds perceive the hazard but make an inappropriate categorical response. Birds that fly into power lines and turbines may be simply not looking ahead or are flying in conditions in which their resolution is very low. Reducing collisions requires far more than attempting to make hazards more conspicuous to humans. It requires recognition of the birds’ perceptual limitations and their distraction away from hazard sites. This requires taking account of the particular ecological requirements and sensory capacities of each target species.
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Siklos, Pierre L. Central Banks into the Breach. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190228835.001.0001.

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The book covers the global economy and the various pressures faced by central banks. It also provides some ideas for reforming existing monetary policy strategies. The events of the past fifteen years in monetary policy are essentially the story of two mistakes, one triumph, and the real possibility of another mistake to come. Prior to the global financial crisis, many central bankers were glib about the connection between finance and the real economy. This is partly because the last three decades saw many financial crises with apparently little lasting impact on the global economy. Another mistake was the failure to adequately appreciate how interconnected the world’s financial systems had become. The triumph was the recognition that price stability is a desirable objective. Whether low and stable inflation is the cause or the consequence of economic performance during the past three decades remains hotly debated, however. There is also the prospect of another financial shock to come. The outlook at the end of 2016 is clouded by at least three sets of forces. On the domestic front, central banks face a difficult and protracted exit from ultra-loose monetary policies; it is largely a problem of their own making. There is also an unwillingness to implement needed structural economic reforms that lie outside the scope of monetary policy. On the international front, there is limited appetite for cooperation and differences in views about the proper role and function of central banks. Central banking is not broken, but it is in need of repair.
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Tir, Jaroslav, and Johannes Karreth. Incentivizing Peace. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190699512.001.0001.

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Civil wars are one of the most pressing problems facing the world. Common approaches such as mediation, intervention, and peacekeeping have produced some results in managing ongoing civil wars, but they fall short in preventing civil wars in the first place. This book argues for considering civil wars from a developmental perspective to identify steps to assure that nascent, low-level armed conflicts do not escalate to full-scale civil wars. We show that highly structured intergovernmental organizations (IGOs, e.g. the World Bank or IMF) are particularly well positioned to engage in civil war prevention. Such organizations have both an enduring self-interest in member-state peace and stability and potent (economic) tools to incentivize peaceful conflict resolution. The book advances the hypothesis that countries that belong to a larger number of highly structured IGOs face a significantly lower risk that emerging low-level armed conflicts on their territories will escalate to full-scale civil wars. Systematic analyses of over 260 low-level armed conflicts that have occurred around the globe since World War II provide consistent and robust support for this hypothesis. The impact of a greater number of memberships in highly structured IGOs is substantial, cutting the risk of escalation by over one-half. Case evidence from Indonesia’s East Timor conflict, Ivory Coast’s post-2010 election crisis, and from the early stages of the conflict in Syria in 2011 provide additional evidence that memberships in highly structured IGOs are indeed key to understanding why some low-level armed conflicts escalate to civil wars and others do not.
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Cukierman, Alex. Central Banks. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.64.

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The first CBs were private institutions that were given a monopoly over the issuance of currency by government in return for help in financing the budget and adherence to the rules of the gold standard. Under this standard the price of gold in terms of currency was fixed and the CB could issue or retire domestic currency only in line with gold inflows or outflows. Due to the scarcity of gold this system assured price stability as long as it functioned. Wars and depressions led to the replacement of the gold standard by the more flexible gold exchange standard. Along with restrictions on international capital flows this standard became a major pillar of the post–WWII Bretton Woods system. Under this system the U.S. dollar (USD) was pegged to gold, and other countries’ exchange rates were pegged to the USD. In many developing economies CBs functioned as governmental development banks.Following the world inflation of the 1970s and the collapse of the Bretton Woods system in 1971, eradication of inflation gradually became the explicit number one priority of CBs. The hyperinflationary experiences of the first half of the 20th century, which were mainly caused by over-utilization of the printing press to finance budgetary expenditures, convinced policymakers in developed economies, following Germany’s lead, that the conduct of monetary policy should be delegated to instrument independent CBs, that governments should be prohibited from borrowing from them, and that the main goal of the CB should be price stability. During the late 1980s and the 1990s numerous CBs obtained instrument independence and started to operate on inflation targeting systems. Under this system the CB is expected to use interest rate policy to deliver a low inflation rate in the long run and to stabilize fluctuations in economic activity in the short and medium terms. In parallel the fixed exchange rates of the Bretton Woods system were replaced by flexible rates or dirty floats. The conjunction of more flexible rates and IT effectively moved the control over exchange rates from governments to CBs.The global financial crisis reminded policymakers that, of all public institutions, the CB has a comparative advantage in swiftly preventing the crisis from becoming a generalized panic that would seriously cripple the financial system. The crisis precipitated the financial stability motive into the forefront of CBs’ policy concerns and revived the explicit recognition of the lender of last resort function of the CB in the face of shocks to the financial system. Although the financial stability objective appeared in CBs’ charters, along with the price stability objective, also prior to the crisis, the crisis highlighted the critical importance of the supervisory and regulatory functions of CBs and other regulators. An important lesson from the crisis was that micro-prudential supervision and regulation should be supplemented with macro-prudential regulation and that the CB is the choice institution to perform this function. The crisis led CBs of major developed economies to reduce their policy rates to zero (and even to negative values in some cases) and to engage in large-scale asset purchases that bloat their balance sheets to this day. It also induced CBs of small open economies to supplement their interest rate policies with occasional foreign exchange interventions.
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Book chapters on the topic "Low Resolution Face Recognition"

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Cheng, Zhiyi, Xiatian Zhu, and Shaogang Gong. "Low-Resolution Face Recognition." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20893-6_38.

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Marciniak, Tomasz, Adam Dabrowski, Agata Chmielewska, and Radosław Weychan. "Face Recognition from Low Resolution Images." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30721-8_22.

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Hernández-Durán, Mairelys, Veronika Cheplygina, and Yenisel Plasencia-Calaña. "Dissimilarity Representations for Low-Resolution Face Recognition." In Similarity-Based Pattern Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24261-3_6.

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Golla, Monika Rani, Poonam Sharma, and Jitendra Madarkar. "Face Recognition Algorithm for Low-Resolution Images." In Social Networking and Computational Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2071-6_29.

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Wang, Xiaoying, Le Liu, and Haifeng Hu. "Coupled Kernel Fisher Discriminative Analysis for Low-Resolution Face Recognition." In Biometric Recognition. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_10.

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Wang, Xuebo, Yao Lu, Xiaozhen Chen, Weiqi Li, and Zijian Wang. "Asymmetric Pyramid Based Super Resolution from Very Low Resolution Face Image." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_59.

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Kono, Yuki, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, and Hiroshi Murase. "Frontal Face Generation from Multiple Low-Resolution Non-frontal Faces for Face Recognition." In Computer Vision – ACCV 2010 Workshops. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22822-3_18.

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Arya, K. V., Shyam Singh Rajput, and Shambhavi Upadhyay. "Noise-Robust Low-Resolution Face Recognition Using SIFT Features." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1135-2_49.

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Jia, Guangheng, Xiaoguang Li, Li Zhuo, and Li Liu. "Recognition Oriented Feature Hallucination for Low Resolution Face Images." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48896-7_27.

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Mudunuri, Sivaram Prasad, Shashanka Venkataramanan, and Soma Biswas. "Improved Low Resolution Heterogeneous Face Recognition Using Re-ranking." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0020-2_39.

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Conference papers on the topic "Low Resolution Face Recognition"

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Wilman, W. W. Zou, and Pong C. Yuen. "Very low resolution face recognition problem." In 2010 IEEE Fourth International Conference On Biometrics: Theory, Applications And Systems (BTAS). IEEE, 2010. http://dx.doi.org/10.1109/btas.2010.5634490.

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Xu, Yong, and Zhong Jin. "Down-Sampling Face Images and Low-Resolution Face Recognition." In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, 2008. http://dx.doi.org/10.1109/icicic.2008.234.

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Rajawat, Aparna, Mahendra Kumar Pandey, and Shyam Singh Rajput. "Low resolution face recognition techniques: A survey." In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT). IEEE, 2017. http://dx.doi.org/10.1109/ciact.2017.7977381.

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Roshna, N. R., and S. Naveen. "Multimodal low resolution face recognition using SVD." In 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT). IEEE, 2017. http://dx.doi.org/10.1109/iccpct.2017.8074200.

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Lei, Zhen, Timo Ahonen, Matti Pietikainen, and Stan Z. Li. "Local frequency descriptor for low-resolution face recognition." In Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771391.

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Wang, Haihan, and Shangfei Wang. "Low-Resolution Face Recognition Enhanced by High-Resolution Facial Images." In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). IEEE, 2023. http://dx.doi.org/10.1109/fg57933.2023.10042552.

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Lai, Shun-Cheung, Chen-Hang He, and Kin-Man Lam. "Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803782.

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Huang, Shih-Ming, Yang-Ting Chou, Szu-Hua Wu, and Jar-Ferr Yang. "Multi-Resolution Local Probabilistic Approach for Low Resolution Face Recognition." In 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI). IEEE, 2011. http://dx.doi.org/10.1109/icbmi.2011.67.

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Tan, Jin Chyuan, Kian Ming Lim, and Chin Poo Lee. "Enhanced AlexNet with Super-Resolution for Low-Resolution Face Recognition." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527433.

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Chai, Jacky Chen Long, Cheng Yaw Low, and Andrew Beng Jin Teoh. "DIRA: disjoint-identity resolution adaptation for low-resolution face recognition." In Fourteenth International Conference on Digital Image Processing (ICDIP 2022), edited by Yi Xie, Xudong Jiang, Wenbing Tao, and Deze Zeng. SPIE, 2022. http://dx.doi.org/10.1117/12.2644258.

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Reports on the topic "Low Resolution Face Recognition"

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Inter-American Development Bank Group Climate Change Action Plan 2021-2025. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003153.

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The Climate Change Action Plan describes the IDB Groups progress since 2016 to support the regions need for low-carbon and climate-resilient development finance and its plan to raise climate ambition continuously in the region. The Second Update to the Institutional Strategy specifies that cross cutting issues, including climate change, continue to hamper development and that the IDB Group will renew its commitment to address them. The climate-finance goal set in the Bahamas Resolution has been extended through its inclusion in the IDB Group Corporate Results Framework 2020- 2023 (CRF 20202023).5 At the same time, all MDBs have committed to complement tracking of their financial contributions to climate action with a new approach focused on the consistency of their support with long-term decarbonization and climate resilience efforts. To this end, MDBs have outlined a common approach to support countries to deliver on their commitments under the PA. There has also been increasing recognition of the need to measure the results of the IDB Groups climate action and the complexity it entails.
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