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 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. Similarly, other sources of information such as text, provide the context for recognition. 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


Alumni

  • Gary B. Huang
  • Vidit Jain

    • Collaborators


    Publications

    • Andrew Kae*, Kihyuk Sohn*, Honglak Lee and Erik Learned-Miller.
      Augmenting CRFs with Boltzmann machine shape priors for image labeling.
      IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
      *The first and second authors made equal contributions and should be considered co-first authors.
      [pdf]
    • Gary B. Huang, Marwan Mattar, Honglak Lee, and Erik Learned-Miller.
      Learning to align from scratch.
      In Neural Information Processing Systems (NIPS), 2012.
      [pdf]
    • Gary B. Huang, Honglak Lee, and Erik Learned-Miller.
      Learning hierarchical representations for face verification.
      IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
      [pdf]
    • Gang Hua, Ming-Hsuan Yang, Erik Learned-Miller, Yi Ma, Matthew Turk, David J. Kriegman, and Thomas S. Huang.
      Introduction to the special section on real-world face recognition.
      IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 33, No. 10, pp. 1921-1924, 2011.
      [pdf]
    • Vidit Jain and Erik Learned-Miller.
      Online domain-adaptation of a pre-trained cascade of classifiers.
      IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
      [pdf]
    • Ralph E. Miller, Erik Learned-Miller, Peter Trainer, Angela Paisley and Volker Blanz.
      Early diagnosis of acromegaly: computers vs clinicians.
      Clinical Endocrinology, Vol. 75, No. 2, pp. 226-231, 2011.
      [pdf]
    • Vidit Jain and Erik Learned-Miller.
      FDDB: A benchmark for face detection in unconstrained settings.
      UMass Amherst Technical Report UM-CS-2010-009, 2010.
      [pdf]
    • Gary B. Huang, Michael J. Jones, and Erik Learned-Miller.
      LFW Results Using a Combined Nowak Plus MERL Recognizer.
      Faces in Real-Life Images Workshop in European Conference on Computer Vision (ECCV), 2008.
      [pdf]
    • Gary B. Huang, Marwan Mattar, Tamara Berg, and Erik Learned-Miller.
      Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments.
      Faces in Real-Life Images Workshop in European Conference on Computer Vision (ECCV), 2008.
      [pdf]
    • Gary B. Huang, Manjunath Narayana, and Erik Learned-Miller.
      Towards unconstrained face recognition.
      The Sixth IEEE Computer Society Workshop on Perceptual Organization in Computer Vision IEEE CVPR, 2008.
      [pdf]
    • 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.