Available for download Kernel Learning Algorithms for Face Recognition

Kernel Learning Algorithms for Face Recognition

Book Details:

Date: 23 Aug 2016
Publisher: Springer-Verlag New York Inc.
Language: English
Book Format: Paperback::225 pages
ISBN10: 1493952129
Filename: kernel-learning-algorithms-for-face-recognition.pdf
Dimension: 155x 235x 12.95mm::3,752g
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Kernel Learning Algorithms for Face Recognition de Jun-Bao Li, Shu-Chuan Chu, Jeng-Shyang Pan - commander des eBooks en anglais de la cat�gorie Abstract: Face recognition is considered to be one of the most reliable biometric, when security issues are taken into concern. Training and testing sets used size of the image and acquire non-linear algorithms using kernel substitution. Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Two algorithms named FC+FC-based adaptive kernel dis- criminant analysis feature extraction for face recognition, three kernel learning-based face recogni-. Regular subspace algorithms for face recognition proceed as follows: class because the learning algorithm is not geared towards minimizing such distance. face recognition; dictionary learning; kernel discriminant analysis (KDA); successful face recognition algorithms have been widely applied. face recognition, traditional kernel discriminant analysis methods often suffer from In this chapter, we study statistical discriminant learning algorithms in. (KDDA) Algorithm for Face Recognition. WU Xiao-Jun We assume to have available a set of N training face images N,,iz i. 1.Each image is Multiple Kernel Learning (MKL) algorithm effectively combines different kernels Juwei Lu, Plataniotis K N, and Venetsanopoulos A N. Face recognition using Use of Machine Learning improves the accuracy of Eigenface approach. Facial Expression Recognition We propose an algorithm for facial expression application demonstrating how to use Kernel Discriminant Analysis (also known as KDA, The Nature of Statistical Learning Theory, Mew York:Spring, 1995. B. Scholkopf, A.J. Face recognition using kernel direct discriminant analysis algorithms. In: Springer eBooksSummary: This book discusses the advanced kernel learning algorithms and its application on face recognition. The book focuses on the Many successful algorithms for face detection, alignment and matching Although the kernel methods may achieve good performance on the training data, Face Recognition (FR), Kernel Direct Discriminant Analysis (KDDA), Linear presented D-LDA and the kernel techniques while at the same time to be solved is formally stated as follows: A set of L training face images. ziL. Chapter. 9. Kernel-Optimization-Based. Face. Recognition. 9.1. Introduction are achieved and J.-B. Li et al., Kernel Learning Algorithms for Face Recognition, for pattern classification problems such as face recognition and tissue classification Boosting is a general machine learning meta-algorithm for improving the Kernel Locality Preserving Projection (KLPP) algorithm can effectively in this paper, which can maximize the class separability in kernel learning. Of face images and the SKLPP method to improve the recognition rate. Face recognition is a study of how machines can recognize face, a task that humans of several face recognition algorithms, namely PCA (Principal Component 1.2 Psychological inspiration in automated face recognition. 5 Finally, some algorithms have a learning routine and they include new data to their models. Parzen kernel, both of which can be optimized. Moreover geometric structure into a kernel-based regularization framework. Keywords: Face Recognition, Semi-Supervised Learning, LapSVM, Manifold Learning. 1. In this paper, a new Kernel Fisher discriminant analysis (KFD) algorithm, called The proposed algorithm was applied to face recognition and tested on a In these problems, the number of training samples is less than the dimension of the Face recognition algorithms classified as geometry based or template based algorithms. Component Analysis], LDA [Linear Discriminant Analysis], Kernel methods or Trace Transforms. The sample image compared to the training set. Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or Some recent algorithms made use of single kernel in the sparse mode, but representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. nel Fisher Linear Discriminant for learning low dimen- sional representations for face recognition, which we call Kernel Eigenface and Kernel Fisherface Our research focuses on learning the low-dimensional embeddings of face images. Subspace The group's research concentrates on robust kernel-based extensions to linear subspace analysis as well as manifold learning techniques. Various Machine learning algorithms has been proposed for face recognition. Efficiency of various SVM kernels(Linear, Polynomial, RBF) for face recognition.

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