Frankenstein: Learning Deep Face Representations using Small Data Guosheng Hu, Xiaojiang Peng, Yongxin Yang, Timothy Hospedales, Jakob Verbeek To cite this version:

Source URL: hal.inria.fr

• Artificial intelligence
• Learning
• Machine learning
• Computational neuroscience
• Applied mathematics
• Face recognition
• Artificial neural networks
• Computer vision
• Deep learning
• Convolutional neural network
• Eigenface
• Facial recognition system

Microsoft PowerPoint - cs559f15_Week5.pptx

Source URL: www.cs.stevens.edu

• Computer vision
• Artificial intelligence
• Vision
• Multivariate statistics
• Face recognition
• Machine learning
• California Institute of Technology
• Caltech 101
• Eigenface
• MNIST database
• Principal component analysis
• Data set

Face2Face: Real-time Face Capture and Reenactment of RGB Videos Justus Thies1 Michael Zollh¨ofer2 Marc Stamminger1 Christian Theobalt2 Matthias Nießner3 1 University of Erlangen-Nuremberg 2 Max-Planck-Institute for Inf

Source URL: www.graphics.stanford.edu

• Computer animation
• Computer graphics
• Visual arts
• Humancomputer interaction
• Computer art
• 3D computer graphics
• Computer facial animation
• Facial motion capture
• Motion capture
• Facial recognition system
• Color space
• RGB color model

REAL TIME 3D HEAD POSE ESTIMATION: RECENT ACHIEVEMENTS AND FUTURE CHALLENGES Gabriele Fanelli, Juergen Gall, Luc Van Gool Computer Vision Laboratory - ETH Zurich ABSTRACT Most automatic face recognition algorithms try to

Source URL: files.is.tue.mpg.de

Object Detection in 2D and 3D Images Fall 2016 Course Syllabus Course Overview: Ever wondered how your digital camera is able to find your face in the view finder (that square which hovers around your face!)? Or how Face

Source URL: cs.sonoma.edu

• Computer vision
• Artificial intelligence
• Vision
• Surveillance
• Face recognition
• Object detection
• Face detection
• Image segmentation
• OpenCV
• Outline of object recognition
• Image processing
• Facial recognition system

2004 Paper 9 Question 11 Computer Vision (a) Why is the performance of current face recognition algorithms so poor? Address both what makes the problem domain intrinsically so challenging, and the shortcomings of the st

Source URL: www.cl.cam.ac.uk

3D face recognition from multiple images: a shape-from-motion approach Manuel Marques and Jo˜ao Costeira Institute for Systems and Robotics (ISR) - Instituto Superior T´ecnico(IST) Av. Rovisco PaisLisboa PO

Source URL: users.isr.ist.utl.pt

Who is There? Video Surveillance & Face Recognition Anil K. Jain Michigan State University March 1, 2016

Source URL: biometrics.cse.msu.edu

To appear in Proc. IEEE IJCB, 2014 Unconstrained Face Recognition: Establishing Baseline Human Performance via Crowdsourcing Lacey Best-Rowden,1 Shiwani Bisht,2 Joshua C. Klontz,3 Anil K. Jain1 1

Source URL: www.cse.msu.edu

To appear: IEEE Transactions on Information Forensics and Security Face Recognition Performance: Role of Demographic Information Brendan F. Klare, Member, IEEE, Mark J. Burge, Senior Member, IEEE, Joshua C. Klontz, Rich

Source URL: www.cse.msu.edu