PPT-Face Recognition

Author : conchita-marotz | Published Date : 2015-10-05

using Convolutional Neural Network and Simple Logistic Classifier Hurieh Khalajzadeh Mohammad Mansouri Mohammad Teshnehlab Table of Contents Convolutional Neural

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Face Recognition: Transcript

using Convolutional Neural Network and Simple Logistic Classifier Hurieh Khalajzadeh Mohammad Mansouri Mohammad Teshnehlab Table of Contents Convolutional Neural Networks Proposed CNN structure for face recognition. :. A Literature Survey. By:. W. Zhao, R. Chellappa, P.J. Phillips,. and A. Rosenfeld. Presented By:. Diego Velasquez. Contents . Introduction. Why do we need face recognition?. Biometrics. Face Recognition by Humans. S. Liao, A. K. Jain, and S. Z. Li, "Partial Face Recognition: Alignment-Free Approach", . IEEE Transactions on Pattern Analysis and Machine Intelligence. , Vol. 35, No. 5, pp. 1193-1205, May 2013, . Microsoft Corporate. ganghua@microsoft.com. Online Contextual Face Recognition: . Towards Large Scale Photo Tagging for Sharing. Photo sharing has become a main online social activity. FaceBook. receives 850 million photo uploads/month. Weihong Deng (. 邓伟洪. ). Beijing Univ. Post. & Telecom.(. 北京邮电大学. ) . 2. Characteristics of Face Pattern. The facial shapes are too similar, sometimes identical ! (~100% face detection rate, kinship verification). . hongliang. . xue. Motivation. . Face recognition technology is widely used in our lives. . Using MATLAB. . ORL database. Database. The ORL Database of Faces. taken between April 1992 and April 1994 at the Cambridge University Computer . Shengcai. Liao. NLPR, CASIA. April 29, 2015. Background. Cooperated face recognition. People are asked to stand in front of a camera with good illumination conditions. Border pass, access control, attendance, etc.. Image Understanding . Xuejin Chen. Face . Recogntion. Good websites. http://www.face-rec.org/. Eigenface. [. Turk & . Pentland. ]. Image Understanding, Xuejin Chen . Eigenface. Projecting a new image into the subspace spanned by the . Feng. . Cen. Outline. R. ecent . advances . in face recognition (FR). Our research work on occluded FR. Face Recognition: applications. Biometrics / access control. No action required. Scan many people at once. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Student: . Yikun. Jiang. . Professor: Brendan Morris. Outlines. Introduction of Face Recognition. The . Eigenface. Approach. Relationship to Biology and Neutral Networks. 16/03/2011. 1. Rui. Min. Multimedia Communications Dept.. EURECOM. Sophia . Antipolis. , France. min@eurecom.fr. Abdenour. . Hadid. . Machine Vision Group. University of Oulu. Oulu, Finland. hadid@ee.oulu.fi. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Hao Zhang. Computer Science Department. 1. Problem Statement. Verification. Identification. A. B. Same / Different persons?. A. B. C. D. Which has the same identity as A?. 2. Solutions. Extensions of still face recognition algorithms. Linda Shapiro. ECE P 596. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Review of Rowley face . d. etector. Overview of. . Viola Jones face detector with .

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