Research Overview

I work in the areas of computer vision and machine learning, developing techniques that leverage large collections of real-world images for a variety of applications. I am particularly interested in the use of intermediate representations such as parts and attributes, both for improving performance on classical vision tasks such as search and recognition, as well as for creating novel applications such as automatic face replacement and exploration of image collections. Much of my work is directed toward human faces.


Watch a recent talk I gave at UNC-Chapel Hill describing my PhD. work.


neeraj headshot

Curriculum Vitae

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Recent Publications (see all)

  1. "Fusing with Context: a Bayesian Approach to Combining Descriptive Attributes," (oral presentation)
    Walter Scheirer, Neeraj Kumar, Karl Ricanek, Terrance E. Boult, Peter N. Belhumeur,
    Proceedings of the IEEE International Joint Conference on Biometrics (IJCB),
    October 2011.
  2. "Two faces are better than one: Face recognition in group photographs,"
    Ohil K. Manyam, Neeraj Kumar, Peter N. Belhumeur, David J. Kriegman,
    Proceedings of the IEEE International Joint Conference on Biometrics (IJCB),
    October 2011.
  3. "Describable Visual Attributes for Face Verification and Image Search," (in publication)
    Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, Shree K. Nayar,
    IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
    October 2011.
  4. "Localizing Parts of Faces Using a Consensus of Exemplars,"
    Peter N. Belhumeur, David W. Jacobs, David J. Kriegman, Neeraj Kumar,
    Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
    June 2011.
  5. "Attribute and Simile Classifiers for Face Verification," (oral presentation)
    Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, Shree K. Nayar,
    Proceedings of the 12th IEEE International Conference on Computer Vision (ICCV),
    October 2009.

Recent Projects (see all)

Localizing Parts of Faces Using a Consensus of Exemplars

Localizing Parts of Faces Using a Consensus of Exemplars

Fall 2009 - Present

Robustly localizing face parts using local detectors and non-parametric, exemplar-based global models

Keywords: face parts, localization, fiducial points, exemplars, non-parametric, real-world
Leafsnap: An Electronic Field Guide

Leafsnap: An Electronic Field Guide

Summer 2009 - Present

Automatically identifying plant species using photos of leaves taken in the field by mobile devices

Keywords: species identification, electronic field guide, curvature histograms, education, leafsnap, smithsonian, botany
Attribute and Simile Classifiers for Face Verification

Attribute and Simile Classifiers for Face Verification

Spring 2009 - Present

Identifying faces using describable visual attributes of the face, automatically detected using attribute and simile classifiers

Keywords: face verification, attributes, similes, attribute classifiers
FaceTracer: A Search Engine for Large Collections of Images with Faces

FaceTracer: A Search Engine for Large Collections of Images with Faces

Spring 2008 - Present

Searching face images using automatically-trained classifiers of descriptions of face and image attributes

Keywords: face search, attributes, attribute classifiers, content-based image retrieval
Face Swapping: Automatically Replacing Faces in Photographs

Face Swapping: Automatically Replacing Faces in Photographs

Fall 2007 - Fall 2008

Using large collections of images to automatically replace faces in photos

Keywords: face swapping, large image collections, face replacement, deidentification, privacy protection

Databases

PubFig: Public Figures Face Database

PubFig: Public Figures Face Database

Fall 2009

60,000 face images with several face verification benchmarks & 65 automatically computed attribute labels for 42,000 images

Keywords: real-world faces, public figures, face verification, attributes
FaceTracer Database

FaceTracer Database

Fall 2008

15,000 face images with detailed pose and fiducial information & 5,000 manual attribute labels

Keywords: real-world faces, attributes, attribute classifiers