Download PDF by Daijin Kim, Jaewon Sung: Automated face analysis : emerging technologies and research

By Daijin Kim, Jaewon Sung

ISBN-10: 1605662178

ISBN-13: 9781605662176

Considering the fact that study on face popularity all started within the 1960's, the sector has speedily widened to automatic face research together with face detection, facial gesture reputation, and facial features attractiveness.

Automated Face research: rising applied sciences and Research presents theoretical historical past to appreciate the general configuration and not easy challenge of automatic face research platforms, that includes a accomplished evaluation of contemporary study for the sensible implementation of the research approach. A must-read for practitioners and scholars within the box, this ebook offers knowing by way of systematically dividing the topic into a number of subproblems akin to detection, modeling, and monitoring of the face.

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2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14, 21-30. , & Movellan, J. (2005). A generative framework for real time object detection and classification. Computer Vision and Image Understanding, 98, 182-210. , & Collobert, D. (1997). A constrained generative model applied to face detection. In Proceedings of the Fourteenth National Conference on Artificial Intelligence. , & Schapire, R. (1999). A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5), 771-780.

The eye disguise discrimination method uses cumulative density function (CDF) because it will be defined with the probability where the event will occur from the specified interval. 4) where c is the confidence value. The probability is less than 1 – c in the n images, where the probability that the number of false detected images is greater than x. Fig. 5. From Fig. 2 (b), we know that the value of x, which satisfies Eq. 5), is 13. 9. 5) Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

3GHz system. 1 Facial Disguise Discrimination To evaluate facial disguise discrimination, we make a subset of the AR face database called AR-FDD that has the purpose of testing facial disguise discrimination. The AR-FDD face database contains 1086 images (96 people × 2 different conditions × 3 different illuminations × 1 or 2 sessions, some people has first session). It has 2 different conditions which are the wearing sun-glass or mask, and it consists of 3 different illuminations such as the normal illumination, the right illumination, and the left illumination.

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Automated face analysis : emerging technologies and research by Daijin Kim, Jaewon Sung


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