Face Processing

Overview

Faces are among the most important elements in images and video. Detection and recognition of faces gives us essential context and meaning about what is happening in a scene. For this and many other reasons, face processing is among the most important research areas in computer vision.


At UMass, we are actively doing research in several key areas of face processing research, including:


These three areas are closely related. While better recognition is our final goal, better alignment improves the performance of recognition algorithms that condition on spatial location, and depend upon spatial relationships within the face. Also, our database, Labeled Faces in the Wild, is designed to help researchers study the problem of unconstrained face recognition, and unconstrained face alignment.


Faculty


Graduate Students


Collaborators


Publications

  • Gary B. Huang, Manu Ramesh, Tamara Berg and Erik Learned-Miller.
    Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments.
    UMass Amherst Technical Report 07-49, 11 pages, 2007.
    [pdf]
  • Andras Ferencz, Erik Learned-Miller, and Jitendra Malik.
    Learning to locate informative features for visual identification.
    To appear International Journal of Computer Vision: Special Issue on Learning and Vision, 2007.
    [pdf]
  • Gary B. Huang, Vidit Jain, and Erik Learned-Miller.
    Unsupervised joint alignment of complex images.
    International Conference on Computer Vision (ICCV), 2007.
    [pdf]
  • Vidit Jain, Erik Learned-Miller, and Andrew McCallum.
    People-LDA: Anchoring topics to people using face recognition.
    International Conference on Computer Vision (ICCV), 2007.
    [pdf]
  • Vidit Jain, Andras Ferencz and Erik Learned-Miller.
    Discriminative training of hyper-feature models for object identification.
    Proceedings of the British Machine Vision Conference (BMVC), Volume 1, pp. 357-366, 2006.
    [pdf]
  • Erik Learned-Miller, Qifeng Lu, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, and Ralph E. Miller.
    Detecting acromegaly: Screening for disease with a morphable model.
    Medical Image Computing and Computer-Assisted Intervention (MICCAI), Volume 2, pp. 495-503, 2006.
    [pdf]
  • Andras Ferencz, Erik Learned-Miller, and Jitendra Malik.
    Building a classification cascade for visual identification from one example.
    In International Conference on Computer Vision (ICCV), pp. 286-293, 2005.
    [pdf]
  • Andras Ferencz, Erik Learned-Miller and Jitendra Malik.
    Learning hyper-features for visual identification.
    In Neural Information Processing Systems (NIPS) 17, pp. 425-432, 2005.
    [pdf]
  • Erik Learned-Miller, Qifeng Lu, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, and Ralph Miller.
    Early diagnosis of acromegaly by facial pattern recognition.
    Abstract for The Ninth International Pituitary Congress, San Diego, CA, 2005.