Abstract
In this paper we propose a new face recognition method based on the weightless neural network system [1]. The algorithm uses 5-pixel n-tuples to map images, which passes through a ranking transform to obtain a binary n-tuple state. A digital neural network correlates the recurring states obtained from the current input pattern to those extracted from the test set. The data used in this paper is from the MIT-CBCL facial database [2], and the training data and testing data set each consist of 10 individual persons, with 100 examples of each subject. An error rate of 0.1% FAR and 0.1% FRR was achieved on data which was totally independent of the training set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lauria, S., Mitchell, R.J.: Weightless Neural Nets for Face Recognition: a Comparison. In: IEEE Signal Processing Society Workshop (1998)
Weyrauch, B., Huang, J., Heisle, B., Blanz, V.: Component-based Face Recognition with 3D Morphable Models (2004)
Samer Charifa, M., Suliman, A., Bikdash, M.: Face Recognition Using a Hybrid General Backpropagation Neural Network. In: 2007 IEEE International Conference on Granular Computing (2007)
Nazeer, S.A., Omar, N., Khalid, M.: Face Recognition using Artificial Neural Networks Approach. In: ICSCN 2007, pp. 420–425. MIT Campus, Anna University (2007)
Bojkovic, Z., Samcovic, A.: Face Detection Approach In Neural Network Based Method For Video Surveillance. In: NEUREL 2006. IEEE (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khaki, K., Stonham, T.J. (2012). Face Recognition with Weightless Neural Networks Using the MIT Database. In: Kamel, M., Karray, F., Hagras, H. (eds) Autonomous and Intelligent Systems. AIS 2012. Lecture Notes in Computer Science(), vol 7326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31368-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-31368-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31367-7
Online ISBN: 978-3-642-31368-4
eBook Packages: Computer ScienceComputer Science (R0)