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Face recognition has been actively studied over the past decade and continues to be a big research challenge. Just recently, researchers have begun to investigate face recognition under unconstrained conditions. Unconstrained Face Recognition provides a comprehensive review of this biometric, especially face recognition from video, assembling a collection of novel approaches that are able to recognize human faces under various unconstrained situations. The underlying basis of these approaches is that, unlike conventional face recognition algorithms, they exploit the inherent characteristics of the unconstrained situation and thus improve the recognition performance when compared with conventional algorithms.
Unconstrained Face Recognition is structured to meet the needs of a professional audience of researchers and practitioners in industry. This volume is also suitable for advanced-level students in computer science.
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Human Identification Based on Gait is the first book to address gait as a biometric. Biometrics is now in a unique position where it affects most people''s lives. This is especially true of "gait", which is one of the most recent biometrics. Recognizing people by the way they walk and run implies analyzing movement which, in turn, implies analyzing sequences of images, thus requiring memory and computational performance that became available only recently. Human Identification Based on Gait introduces developments from distinguished researchers within this relatively new area of biometrics. This book clearly establishes how human gait is biometric.
Human Identification Based on Gait is structured to meet the needs of professionals in industry, as well as advanced-level students in computer science.
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887 kr
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675 kr
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669 kr
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1 994 kr
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This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition.
This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra.
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16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part II
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The 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022.
The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:
Part I: 3D computer vision; optimization methods;
Part II: applications of computer vision, vision for X; computational photography, sensing, and display;Part III: low-level vision, image processing;
Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics;
Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis;
Part VI: biomedical image analysis; deep learning for computer vision;Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.
16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part VII
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1 587 kr
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The 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022.
The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:
Part I: 3D computer vision; optimization methods;
Part II: applications of computer vision, vision for X; computational photography, sensing, and display;Part III: low-level vision, image processing;
Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics;
Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis;
Part VI: biomedical image analysis; deep learning for computer vision;Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.
16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part III
1 111 kr
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1 427 kr
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The 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022.
The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:
Part I: 3D computer vision; optimization methods;
Part II: applications of computer vision, vision for X; computational photography, sensing, and display;Part III: low-level vision, image processing;
Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics;
Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis;
Part VI: biomedical image analysis; deep learning for computer vision;Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.