Paper
21 September 2001 3D face recognition system using cylindrical hidden-layer neural network: spatial domain and its eigenspace domain
Benyamin Kusumoputro, Martha Yuliana Pangabean, Leila Fatmasari Rachman
Author Affiliations +
Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441446
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China
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 circular-structure of neurons. This neural system is then called as cylindrical-structure of hidden layer neural network (CHL-NN). 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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Benyamin Kusumoputro, Martha Yuliana Pangabean, and Leila Fatmasari Rachman "3D face recognition system using cylindrical hidden-layer neural network: spatial domain and its eigenspace domain", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); https://doi.org/10.1117/12.441446
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KEYWORDS
Neurons

Facial recognition systems

Visualization

Neural networks

3D image processing

Databases

Feature extraction

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