Face Pattern Recognition

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Deep Learning-Based Face Analytics (Advances in Computer Vision and Pattern Recognition)

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Ant Colony Optimized Local Binary Pattern Technique in Face Recognition: Applying Nature Inspired Optimization for Face Re…

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Independent Component Analysis of Edge Information for Face Recognition (SpringerBriefs in Applied Sciences and Technology)

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Face Recognition Across the Imaging Spectrum

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Full print is visible on the front and reverse Microfiber polyester with a slightly transparent effect Hand wash only. Do not dry clean or tumble dry.

The Independent Component Analysis (ICA) plays very important role in blind source separation and has many more applications in pattern recognition. The ICA is new area for researchers in the last decade for face recognition. There is much more scope for research using ICA for face recognition with different methods of feature extractions and needs to be addressed. As the promising applications of ICA is feature extraction, where it extracts independent image bases which are not necessarily orthogonal and it is sensitive to high order statistics. In the task of face recognition, important information may be contained in the high order relationship among pixels. Independent Component Analysis (ICA) minimizes both second order and higher-order dependencies in the input data and attempts to find the basis along with the data when projected onto them are statistically independent. So ICA seems to be a promising face feature extraction method. Face Recognition Using Independent Component Analysis (Paperback)

Recently classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. There are numbers of different Decision Support System (DSS) that has developed to operate on the minimum input data set or the output data set to give the correct decision. A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the face recognition performance. In this book, a face recognition system has been developed by applying multi-classifier fusion on the output of the three different classification methods namely Artificial Neural Network, Genetic Algorithm and Euclidean distance measure based on the Principal Component Analysis dimensionality reduction technique. Experimental results and performance analysis show the comparison results between multi-classifier fusion based face recognition system with individual classifier performance. Face Recognition Using Multiple Classifier Fusion (Paperback)

Pattern recognition has gained significant attention due to the rapid explosion of internet- and mobile-based applications. Among the various pattern recognition applications, face recognition is always being the center of attraction. With so much of unlabeled face images being captured and made available on internet (particularly on social media), conventional supervised means of classifying face images become challenging. This clearly warrants for semi-supervised classification and subspace projection. Another important concern in face recognition system is the proper and stringent evaluation of its capability. This book is edited keeping all these factors in mind. This book is composed of five chapters covering introduction, overview, semi-supervised classification, subspace projection, and evaluation techniques. Face Recognition: Semisupervised Classification, Subspace Projection and Evaluation Methods (Hardcover)

Advances in Face Image Analysis: Theory and applications describes several approaches to facial image analysis and recognition. Eleven chapters cover advances in computer vision and
pattern recognition methods used to analyze facial data. The topics addressed in this book include automatic face detection, 3D face model fitting, robust face recognition, facial expression recognition, face image data embedding, model-less 3D face pose estimation and image-based age estimation. The chapters are also written by experts from a different research groups. Readers will, therefore, have access to contemporary knowledge on facial recognition with some diverse perspectives offered for individual techniques. The book is a useful resource for a to a wide audience such as i) researchers and professionals working in the field of face image analysis, ii) the entire pattern recognition community interested in processing and extracting features from raw face images, and iii) technical experts as well as postgraduate computer science students interested in cutting edge concepts of facial image recognition. Advances in Face Image Analysis: Theory and applications (Paperback)

This book deals with a case: the elucidation of the intricate and detailed patterns of the human face using the tools of ‘pattern theory’, of statistical pattern recognition and of differential geometry. It is an outcome of the work at the Harvard Robotics Laboratory in the early 1990s. The human face is perhaps the most familiar and easily recognized object in the world, yet both its three-dimensional shape and its two-dimensional images are complex and hard to characterize. This book develops the vocabulary of ridges and parabolic curves, of illumination eigenfaces and elastic warpings for describing the perceptually salient features of a face and its images. The book also explores the underlying mathematics and applies these mathematical techniques to the computer vision problem of face recognition, using both optical and range images. Two- And Three-Dimensional Patterns of the Face (Paperback)