Titlebook: Kernel Learning Algorithms for Face Recognition; Jun-Bao Li,Shu-Chuan Chu,Jeng-Shyang Pan Book 2014 Springer Science+Business Media New Yo

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Statistical Learning-Based Face Recognition,ys on from a single viewpoint of application, method, or condition, this book has a comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions. In the sensory inputs, we review face recognition from image-based, video-based, 3D-based, an

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Kernel Principal Component Analysis (KPCA)-Based Face Recognition,processing, feature extraction based no dimensionality reduction plays the important role in the relative areas. Feature extraction simplifies the amount of resources required to describe a large set of data accurately for classification and clustering. On the algorithms, when the input data are too

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Kernel Manifold Learning-Based Face Recognition,n aims to develop a meaningful low-dimensional subspace in a high-dimensional input space such as PCA and LDA. LDA is to find the optimal projection matrix with Fisher criterion through considering the class labels, and PCA seeks to minimize the mean square error criterion.

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Kernel-Learning-Based Face Recognition for Smart Environment,e implemented in an ambient space. Multiple sensors such as audio and video are used for identification and ensure the safety. The wrist pulse signal detector is used to health analysis. These multisensor multimedia systems are be recording, processing, and analyzing the sensory media streams and pr

斥責

Kernel Construction for Face Recognition, recognition algorithm should sufficiently consider the following two issues: what features are used to represent a face image and how to classify a new face image based on this representation. So the facial feature extraction plays an important role in face recognition.

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Jun-Bao Li,Shu-Chuan Chu,Jeng-Shyang Pancal solution allow analyses of the noise transmission from the source to the passenger ear. Within this contribution, numerical methods are applied within a multidisciplinary simulation chain resulting in a prediction of cabin noise due to fluid noise sources. Noise by the jet and the turbulent boun