3-D face recognition system using cylindrical-hidden layer neural network: Spatial domain and its eigenspace domain

Benyamin Kusumo Putro, Martha Yuliana Pangabean, Leila Fatmasari Rachman

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

In this paper, a 3-D face recognition system is developed using a modified neural network. This modified neural network is constructed by substituting each of neuron in its hidden layer of conventional multilayer perceptron with a circularstructure of neurons. This neural system is then called as cylindrical-structure of hidden layer neural network (CHLNN). The neural system is then applied on a real 3-D face image database that consists of 5 Indonesian persons. The images are taken under four different expressions such as neutral, smile, laugh and free expression. The 2-D images is taken from the human face images by gradually changing visual points, which is done by successively varies the camera position from -90 to +90 with an interval of 15 degree. The experimental result has shown that the average recognition rate of 60% could be achieved when we used the image in its spatial domain. Improvement of the system is then developed, by transforming the image in its spatial domain into its eigenspace domain. Karhunen Loeve transformation technique is used, and each image in the spatial domain is represented as a point in the eigenspace domain. Fisherface method is then utilized as a feature extraction on the eigenspace domain, and using the same database and experimental procedure, the recognition rate of the system could be increased into 84% in average.

Original languageEnglish
Pages (from-to)188-195
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4550
DOIs
Publication statusPublished - 1 Dec 2001
EventImage Extraction, Segmentation, and Recognition - Wuhan, China
Duration: 22 Oct 200124 Oct 2001

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