Title :
Appearance-based face recognition and light-fields
Author :
Gross, Ralph ; Matthews, Iain ; Baker, Simon
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
fDate :
4/1/2004 12:00:00 AM
Abstract :
Arguably the most important decision to be made when developing an object recognition algorithm is selecting the scene measurements or features on which to base the algorithm. In appearance-based object recognition, the features are chosen to be the pixel intensity values in an image of the object. These pixel intensities correspond directly to the radiance of light emitted from the object along certain rays in space. The set of all such radiance values over all possible rays is known as the plenoptic function or light-field. In this paper, we develop a theory of appearance-based object recognition from light-fields. This theory leads directly to an algorithm for face recognition across pose that uses as many images of the face as are available, from one upwards. All of the pixels, whichever image they come from, are treated equally and used to estimate the (eigen) light-field of the object. The eigen light-field is then used as the set of features on which to base recognition, analogously to how the pixel intensities are used in appearance-based face and object recognition.
Keywords :
eigenvalues and eigenfunctions; face recognition; object recognition; appearance based face recognition; appearance based object recognition; light fields; object recognition algorithm; pixel intensity; plenoptic function; scene measurements; Character recognition; Charge coupled devices; Face recognition; Image recognition; Layout; Lighting; Object recognition; Optical sensors; Pixel; Stimulated emission; Algorithms; Artificial Intelligence; Computer Simulation; Face; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Light; Male; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Photography; Photometry; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2004.1265861