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In this book, a new high performance face recognition system based on matching the colour pixel statistics is introduced. A pre-processing;
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face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory;
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face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory;
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implement a face recognition method, using PCA&LDA Algorithm and compare these both algorithms with respect to time, memory and accuracy. Face;
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microcontroller using MATLAB GUI environment; pattern recognition using quaternion colour moments; and pattern recognition by Bessel mask and one;
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shows that face recognition system designed using Fisher Linear Discriminant analysis (FLD) demonstrates the best accuracy of 83%. However, the;
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This book presents a novel approach for Face Recognition using 'Vector Quantization'. Face Recognition is one of the popular biometric;
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This book seeks to comprehensively address the face recognition problem while gaining new insights from complementary fields of endeavor;
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Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced;
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This book discusses human emotion recognition from face images using different modalities, highlighting key topics in facial expression;
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This book discusses human emotion recognition from face images using different modalities, highlighting key topics in facial expression;
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illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: * Provides a self-contained introduction to statistical pattern;
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illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: * Provides a self-contained introduction to statistical pattern;
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Face Recognition: Cognitive and Computational Processes critically discusses current research in face recognition, leading to an original;
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previous research on face recognition had been phenomena driven. Recent empirical work together with the application of computational, mathematical;
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previous research on face recognition had been phenomena driven. Recent empirical work together with the application of computational, mathematical;
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Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and;
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Face recognition has been an active area of research in image processing and computer vision due to its extensive range of prospective;
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Face recognition has been actively studied over the past decade and continues to be a big research challenge. Just recently;
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techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face;
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Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem;
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Although most people are good at face recognition, we are particularly good at recognizing the faces of individuals who share our race;
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Although most people are good at face recognition, we are particularly good at recognizing the faces of individuals who share our race;
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Recognition from Real World Videos in conjunction with the 22nd International Conference on Pattern Recognition held in Stockholm, Sweden, in August;
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3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for;
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focuses on topics such as on-line signature verification, improving the performance of colour spaces in face recognition, block coding schemes;
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