CAMEO: Face Recognition Year 1 Progress and Year 2 Goals Fernando de la Torre, Carlos Vallespi, Takeo Kanade.

Slides:

Advertisements

Similar presentations

Bayesian Decision Theory Case Studies

Advertisements

CVPR2013 Poster Modeling Actions through State Changes.

Face Recognition. Introduction Why we are interested in face recognition? Why we are interested in face recognition? Passport control at terminals in.

Semi-Supervised Hierarchical Models for 3D Human Pose Reconstruction Atul Kanaujia, CBIM, Rutgers Cristian Sminchisescu, TTI-C Dimitris Metaxas,CBIM, Rutgers.

Joint Eye Tracking and Head Pose Estimation for Gaze Estimation

Multi-Local Feature Manifolds for Object Detection Oscar Danielsson Stefan Carlsson Josephine Sullivan

Computer Vision REU Week 2 Adam Kavanaugh. Video Canny Put canny into a loop in order to process multiple frames of a video sequence Put canny into a.

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm Lecture #20.

Face Recognition Under Varying Illumination Erald VUÇINI Vienna University of Technology Muhittin GÖKMEN Istanbul Technical University Eduard GRÖLLER Vienna.

Generic Object Recognition -- by Yatharth Saraf A Project on.

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John.

Research Update and Future Work Directions – Jan 18, 2006 – Ognjen Arandjelović Roberto Cipolla.

CS335 Principles of Multimedia Systems Content Based Media Retrieval Hao Jiang Computer Science Department Boston College Dec. 4, 2007.

Eigenfaces As we discussed last time, we can reduce the computation by dimension reduction using PCA –Suppose we have a set of N images and there are c.

HIWIRE MEETING CRETE, SEPTEMBER 23-24, 2004 JOSÉ C. SEGURA LUNA GSTC UGR.

Automatic Face Recognition for Film Character Retrieval in Feature-Length Films Ognjen Arandjelović Andrew Zisserman.

Face Recognition from Face Motion Manifolds using Robust Kernel RAD Ognjen Arandjelović Roberto Cipolla Funded by Toshiba Corp. and Trinity College, Cambridge.

Facial Recognition CSE 391 Kris Lord.

Selective Transfer Machine for Personalized Facial Action Unit Detection Wen-Sheng Chu, Fernando De la Torre and Jeffery F. Cohn Robotics Institute, Carnegie.

Facial Feature Detection

Learning to classify the visual dynamics of a scene Nicoletta Noceti Università degli Studi di Genova Corso di Dottorato.

Face Alignment Using Cascaded Boosted Regression Active Shape Models

Miguel Reyes 1,2, Gabriel Dominguez 2, Sergio Escalera 1,2 Computer Vision Center (CVC) 1, University of Barcelona (UB) 2

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition by D. Tao, X. Li, and J. Maybank, TPAMI 2007 Presented by Iulian Pruteanu.

Access Control Via Face Recognition Progress Review.

Rotation Invariant Neural-Network Based Face Detection

1 Recognition by Appearance Appearance-based recognition is a competing paradigm to features and alignment. No features are extracted! Images are represented.

Combined Central and Subspace Clustering for Computer Vision Applications Le Lu 1 René Vidal 2 1 Computer Science Department, Johns Hopkins University,

Recognition, Analysis and Synthesis of Gesture Expressivity George Caridakis IVML-ICCS.

UCF REU: Weeks 1 & 2. Gradient Code Gradient Direction of the Gradient: Calculating theta.

A Two-level Pose Estimation Framework Using Majority Voting of Gabor Wavelets and Bunch Graph Analysis J. Wu, J. M. Pedersen, D. Putthividhya, D. Norgaard,

Pattern Recognition April 19, 2007 Suggested Reading: Horn Chapter 14.

Face Recognition: An Introduction

Augmented Reality and 3D modelling By Stafford Joemat Supervised by Mr James Connan.

CAMEO: Year 1 Progress and Year 2 Goals Manuela Veloso, Takeo Kanade, Fernando de la Torre, Paul Rybski, Brett Browning, Raju Patil, Carlos Vallespi, Betsy.

CAMEO: Meeting Understanding Prof. Manuela M. Veloso, Prof. Takeo Kanade Dr. Paul E. Rybski, Dr. Fernando de la Torre, Dr. Brett Browning, Raju Patil,

Face Alignment at 3000fps via Regressing Local Binary Features CVPR14 Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun Presented by Sung Sil Kim.

Jack Pinches INFO410 & INFO350 S INFORMATION SCIENCE Computer Vision I.

EMPATH: A Neural Network that Categorizes Facial Expressions Matthew N. Dailey and Garrison W. Cottrell University of California, San Diego Curtis Padgett.

Data Mining, ICDM '08. Eighth IEEE International Conference on Duy-Dinh Le National Institute of Informatics Hitotsubashi, Chiyoda-ku Tokyo,

Visual Computing Computer Vision 2 INFO410 & INFO350 S2 2015

2D-LDA: A statistical linear discriminant analysis for image matrix

Hierarchical Motion Evolution for Action Recognition Authors: Hongsong Wang, Wei Wang, Liang Wang Center for Research on Intelligent Perception and Computing,

Bayesian Decision Theory Case Studies CS479/679 Pattern Recognition Dr. George Bebis.

Face recognition using Histograms of Oriented Gradients

Unsupervised Learning of Video Representations using LSTMs

Convolutional Neural Network

Guillaume-Alexandre Bilodeau

ROBUST SUBSPACE LEARNING FOR VISION AND GRAPHICS

CALO VISUAL INTERFACE RESEARCH PROGRESS

Recognition with Expression Variations

René Vidal Time/Place: T-Th 4.30pm-6pm, Hodson 301

Recognition using Nearest Neighbor (or kNN)

Machine Learning Ali Ghodsi Department of Statistics

Outline Announcement Local operations (continued) Linear filters

Video-based human motion recognition using 3D mocap data

The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression By: Patrick Lucey, Jeffrey F. Cohn, Takeo.

Outline Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,”

Features Readings All is Vanity, by C. Allan Gilbert,

Identifying Human-Object Interaction in Range and Video Data

Brief Review of Recognition + Context

Patch-Based Image Classification Using Image Epitomes

Interactive media.

Face Detection Gender Recognition 1 1 (19) 1 (1)

Applications Discussion

Strength of relation High Low Number of data Relationship Data

End-to-End Facial Alignment and Recognition

Week 7 Presentation Ngoc Ta Aidean Sharghi

Fig. 1 Fully unified pipeline for wild chimpanzee face tracking and recognition from raw video footage. Fully unified pipeline for wild chimpanzee face.

Presentation transcript:

CAMEO: Face Recognition Year 1 Progress and Year 2 Goals Fernando de la Torre, Carlos Vallespi, Takeo Kanade

Face Recognition from video. – How to learn a facial model from the data coming from the face detector?

Face Recognition from video. Challenges: 1)How to learn INVARIANTLY to spatial transformations? Simultaneous registration and Subspace computation. 2) How to select the most discriminative features? 3) How to deal with missing data?

Face Recognition from video. –Register w.r.t a Subspace –Selecting the most discriminative samples.

Face Recognition from video. Distance between Sets A and B. Singular vectors of A A= B= - How to exploit temporal redundancy in the recognition process?

Face Recognition from video. 95 % of recognition rate (11 Subjects and 30 images per subject).

Plans year 2. Why is hard to perform face recognition from Mosaic images? –Small images. –Noisy images. –Misalignments. But… –Temporal redundancy. –Recognizing several people (exclusive principle). –Superesolution.

Learning person-specific models. Unsupervised learning from video sequences: –Facial appearance models. –Behaviour models (e.g. gestures). Learning person-specific models can be useful to identify people, to predict actions?

Meeting visualization/summarization Input: –Set of several videos, with detected and recognized faces. –Set of indicators if the person is talking, up, down, etc… Output: –Low dimensional visualization of the meeting activity and interaction between people. –Learning interaction models between people.