Citation

Wong, Wai Kit and Lee, Gin Chong and Loo, Chu Kiong and Lock, Raymond (2013) Quaternion Based Fuzzy Neural Network Classifier for MPIK Datasets View-invariant Color Face Image Recognition. Informatica, 37 (2). pp. 181-192. ISSN 0350-5596

Text 37.pdf Restricted to Repository staff only Download (566kB)

Abstract

This paper presents an effective color image processing system view-invariant person face image recognition for Max Planck Institute Kybernetik (MPIK) dataset. The proposed system can recognize face images of view-invariant person by correlating the input face images with the reference face image and classifying them according to the correct persons’ name/ID indeed. It has been carried out by constructing a complex quaternion correlator and a max-product fuzzy neural network classifier. Two classification parameters, namely discrete quaternion correlator output (p-value) and the peak to sidelobe ratio (PSR), were used in classifying the input face images, and to categorise them either into the authentic class or non-authentic class. Besides, a new parameter called G-value is also introduced in the proposed view-invariant color face image recognition system for better classification purpose. Experimental results shows that the proposed view-invariant color face image recognition system outperforms the conventional NMF, BDNMF and hypercomplex Gabor filter in terms of consumption of enrollment time, recognition time and accuracy in classifying MPIK color face images which are view-invariant, noise influenced and scale invariant. Povzetek: Predstavljena je metoda prepoznavanja obrazov, testirana na domeni Max Planck Institute Kybernetik.

Item Type:	Article
Uncontrolled Keywords:	image processing, face recognition, fuzzy neural network classifier, quaternion correlation
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	12 Jan 2017 04:40
Last Modified:	12 Jan 2017 04:40
URII:	http://shdl.mmu.edu.my/id/eprint/6109

Downloads

Edit (login required)