PPT-Video Face Recognition: A Literature Review

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. 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, . USE THE 1-5-1-3 MODEL TO ENHANCE YOUR LECTURE VIDEOS. Presenter: Shilpa Patwardhan. Workshop Learning Outcomes. On completing this workshop, participants will be able to:. List published literature about engagement and learning in video lectures.. 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. An . up-to-date . analysis. and . synthesis . of the scholarly conversation on a given topic. It should tell the reader what arguments scholars have made / are making, how they are making them (method) if necessary, and how the conversation has changed over time. The Lit. Review should demonstrate that you can see the outlines of the larger scholarly conversation on your given . 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. 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 . Dr. Mary Ann Seremeth. 3 M’s. :. ASL Literature Production. ASL. Literature. Production. Maintenance. Measurements. Mastery. Overview.  . 1) . to incorporate the values of video production structure and basic fundamental editing concepts to enhance ASL .