Get Facial Analysis from Continuous Video with Applications to PDF

By Antonio J. Colmenarez

ISBN-10: 1402078021

ISBN-13: 9781402078026

ISBN-10: 140207803X

ISBN-13: 9781402078033

Computing device imaginative and prescient algorithms for the research of video facts are bought from a digicam aimed toward the person of an interactive method. it's in all likelihood valuable to reinforce the interface among clients and machines. those photo sequences offer details from which machines can determine and retain tune in their clients, realize their facial expressions and gestures, and supplement different kinds of human-computer interfaces. Facial research from non-stop Video with functions to Human-Computer Interfaces offers a studying process according to information-theoretic discrimination that is used to build face and facial function detectors. This publication additionally describes a real-time approach for face and facial characteristic detection and monitoring in non-stop video. ultimately, this publication offers a probabilistic framework for embedded face and facial features popularity from photograph sequences. Facial research from non-stop Video with purposes to Human-Computer Interfaces is designed for a certified viewers composed of researchers and practitioners in undefined. This booklet can also be compatible as a secondary textual content for graduate-level scholars in laptop technological know-how and engineering.

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Extra resources for Facial Analysis from Continuous Video with Applications to Human-Computer Interface (International Series on Biometrics)

Sample text

These mod­ els were used in a scale-invariant scheme for detection of faces and facial features. One of the most important issues in applying information-based max­ imum discrimination learning is the technique used for image prepro­ cessing and re-quantization. This is important because of the mapping FACE AND FACIAL FEATURE DETECTION 23 between the input image space and the discrete image space. This map­ ping reduces the number of possible pixel values of the discrete image space while preserving the information useful for object discrimination so that discrete probability models can be implemented.

This technique has been tested in the context of face detection, yielding excellent results. In the following sections, we briefly review the techniques used for face and facial expression recognition. 1 Face recognition The main approach taken for face recognition is to compare some data present in a database with that of the probe image obtained from the person to be recognized. , the graylevel intensity). Several similarity measures and image preprocessing techniques have been used to deal with image variations due to light conditions, head pose, facial expressions, etc.

Then, it is used to further classify the training set, depending on the success of this classification. Then, the samples that were not successfully recognized are used separately in a second stage to reinforce the learning procedure. Information-based maximum discrimination learning can also benefit from error bootstrapping. Once the classifier is obtained with all the examples of the training set, and the training set has been evaluated with this classifier, statistics of the correctly classified examples are computed separately from those of the incorrectly classified examples.

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Facial Analysis from Continuous Video with Applications to Human-Computer Interface (International Series on Biometrics) by Antonio J. Colmenarez

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