Refereed full papers (journals, book chapters, international conferences)

1999

@inproceedings{Mar1999,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Aleix Martinez},
	title =	{Face Image Retrieval using {HMM}s},
	booktitle =	{IEEE Workshop on Content-based Access of Image and Video
	Libraries (CBAIVL'99)},
	address =	{Fort Collins, Colorado, USA},
	pages =	{35--39},
	month =	{June~22},
	year =	{1999},
	url =	{http://rvl1.ecn.purdue.edu/\~{}aleix/cbaivl99.pdf},
	url1 =	{http://rvl1.ecn.purdue.edu/\~{}aleix/cbaivl99.ps.Z},
	abstract =	{This paper introduces a new face recognition system that
	can be used to index (and thus retrieve) images and videos of a
	database of faces. New face recognition approaches are needed because,
	although much progress has been made to identify face taken from
	different viewpoints, we still cannot robustly identify faces under
	different illumination conditions, or when the facial expression
	changes, or when a part of the face is occluded on account of glasses
	or parts of clothing. When face recognition methods have worked in the
	past, it was only when all possible ``image variations'' were learned.
	Principal Components Analysis (PCA) and Fisher Discriminant Analysis
	(FDA) are well-known cases of such methods. 

	In this paper we present a different approach to the indexing of face
	images. Our approach is based on identifying frontal faces and it
	allows reasonable variability in facial expressions, illumination
	conditions, and occlusions caused by eye-wear or items of clothing such
	as scarves. We divide a face image into $n$ different regions, analyze
	each region with PCA, and then use a Bayesian approach to finding the
	best possible global match between a query image and a database image.
	The relationships between the n parts is modeled by using Hidden Markov
	Models (HMMs).},
}