PPT-Lecture: Face Recognition

and Feature Reduction Juan Carlos Niebles and Ranjay Krishna Stanford Vision and Learning Lab 2Nov17 1 Recap Curse of dimensionality Assume 5000 points uniformly distributed in the unit hypercube and we want to apply 5NN Suppose our query point is at the origin. :. 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, . Joseph Miller Michael Osborn Jaclyn . Duket. . Ohn’Jay. Walker. Undergraduate Research. Our thoughts about research in Human Computer Interaction-Design…. Very engaging and large field full of online interactions as well as user based studies that can be carried out. Face & Fingerprint Recognition Terminal. The demand for biometrics is slowly moving toward tighter security, by employing facial recognition for access control.. Convenient and easy to use. Promotes punctuality effortlessly . Joseph Miller Michael Osborn Jaclyn . Duket. . Ohn’Jay. Walker. Date. Description. Overview. 2/1/10-2/7/10. Week 1: Introduction to program, research. List several interesting topics. 2/8/10-2/14/10. 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 . 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. Student: . Yikun. Jiang. . Professor: Brendan Morris. Outlines. Introduction of Face Recognition. The . Eigenface. Approach. Relationship to Biology and Neutral Networks. cogch2 pt 2. 2. Disorders . of Object Recognition. AGNOSIA.  : a general term for a loss of ability to recognize objects, people, sounds, shapes, or smells. . Agnosias result from damage to . cortical areas . Deep Learning for Expression Recognition in Image Sequences Daniel Natanael García Zapata Tutors: Dr. Sergio Escalera Dr. Gholamreza Anbarjafari April 27 2018 Introduction and Goals Introduction Dennis Hamester et al., “Face ExpressionRecognition with a 2-Channel ConvolutionalNeural Network”, International Joint Conference on Neural Networks (IJCNN), 2015. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Face Detection. What kind of features?. 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 . AdaBoost. Linda Shapiro. CSE 455. 1. What’s Coming. The basic . AdaBoost. algorithm (next). The Viola Jones face . d. etector features. The modified . AdaBoost. algorithm that is used in Viola-Jones face detection.