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Showing papers on "Three-dimensional face recognition published in 1990"


Journal ArticleDOI
TL;DR: This study will describe a structural model of fingerprints, based on local structural relations among features, and an associated automated recognition system which addresses the limitations of existing fingerprint models.

255 citations


Proceedings ArticleDOI
16 Jun 1990
TL;DR: Two novel methods for recognizing totally unconstrained handwritten numerals are presented and it is shown that if reliability is of utmost importance, one can avoid substitutions completely and still retain a fairly high recognition rate.
Abstract: Two novel methods for recognizing totally unconstrained handwritten numerals are presented. One classifies samples based on structural features extracted from their skeletons; the other makes use of their contours. Both methods achieve high recognition rates (86.05%, 93.90%) and low substitution rates (2.25%, 1.60%). To take advantage of the inherent complementarity of the two methods, different ways of combining them are studied. It is shown that it is possible to reduce the substitution rate to 0.70%, while the recognition rate remains as high as 92.00% . Furthermore, if reliability is of utmost importance, one can avoid substitutions completely (reliability 100%) and still retain a fairly high recognition rate (84.85%). >

90 citations


Proceedings Article
01 Jan 1990
TL;DR: A near-real-time computer system which can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals is developed.
Abstract: We have developed a near-real-time computer system which can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. Our approach treats the face recognition problem as an intrinsically twodimensional recognition problem, taking advantage of the fact that faces are are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces", because they are the eigenvectors (principal componmt,s) of the set of faces; they do not necessarily correspontl to features such as eyes, ears, and noses.

10 citations


Book ChapterDOI
Lawrence R. Rabiner1
01 Apr 1990
TL;DR: This paper is concerned primarily with showing how pattern recognition techniques have been applied to the problems of isolated word (or discrete utterance) recognition, connected word recognition, and continuous speech recognition.
Abstract: Algorithms for speech recognition can be characterized broadly as pattern recognition approaches and acoustic phonetic approaches. To date, the greatest degree of success in speech recognition has been obtained using pattern recognition paradigms. Thus, in this paper, we will be concerned primarily with showing how pattern recognition techniques have been applied to the problems of isolated word (or discrete utterance) recognition, connected word recognition, and continuous speech recognition. We will show that our understanding (and consequently the resulting recognizer performance) is best for the simplest recognition tasks and is considerably less well developed for large scale recognition systems.

4 citations



Proceedings ArticleDOI
01 Jul 1990
TL;DR: This paper provides a general summary of recent work on shape recognition by humans and suggests two broad modes of visual image processing executed by different cortical loci can be distinguished.
Abstract: The output of most medical imaging systems is a display for interpretation by human observers. This paper provides a general summary of recent work on shape recognition by humans. Two broad modes of visual image processing executed by different cortical loci can be distinguished: a) a mode for motor interaction which is sensitive to quantitative variation in image parameters and b) a mode for basic-level object recognition which is based on a small set of qualitative contrasts in viewpoint invariant properties of images edges. Many medical image classifications pose inherently difficult problems for the recognition system in that they are based on quantitative and surface patch variations--rather than qualitative--variations. But when recognition can be achieved quickly and accurately it is possible that a small viewpoint invariant contrast has been discovered and is being exploited by the interpreter.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

1 citations