Human Face Recognition using Zernike moments and Nearest Neighbor classifier

This paper presents a human face recognition system using an orthogonal invariant moment namely Zernike moment (ZM) as a feature extractor and a simple Euclidean square distance classifier or nearest neighbor. Changes in illumination condition, pose, facial expression and others are challenging task...

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Bibliographic Details
Main Authors: Nor'aini, A.J., Raveendran, P., Selvanathan, N.
Format: Conference Proceeding
Language:English
Subjects:
Euclidean square distance
Face recognition
Feature extraction
Glass
Humans
Image databases
Image recognition
Laboratories
Lighting
nearest neighbor
Nearest neighbor searches
orthogonal invariant moment
Spatial databases
Zernike moments
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Summary:This paper presents a human face recognition system using an orthogonal invariant moment namely Zernike moment (ZM) as a feature extractor and a simple Euclidean square distance classifier or nearest neighbor. Changes in illumination condition, pose, facial expression and others are challenging task in recognizing face images. Solving these problems requires a feature extractor that can generate distinct features for each class of image and a classifier that able to recognize and classify the face image precisely. The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical [1]. They vary in position, rotation, scale, expression, with and without glasses. The performance of the classification depends on the moment order and classification error is observed below 10%.
DOI:10.1109/SCORED.2006.4339322