Face Recognition with Weightless Neural Networks Using the MIT Database
In this paper we propose a new face recognition method based on the weightless neural network system . 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 , 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.
KeywordsFace Recognition False Acceptance Rate False Rejection Rate Face Recognition Method IEEE Signal Processing Society
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- 1.Lauria, S., Mitchell, R.J.: Weightless Neural Nets for Face Recognition: a Comparison. In: IEEE Signal Processing Society Workshop (1998)Google Scholar
- 2.Weyrauch, B., Huang, J., Heisle, B., Blanz, V.: Component-based Face Recognition with 3D Morphable Models (2004)Google Scholar
- 3.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)Google Scholar
- 4.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)Google Scholar
- 5.Bojkovic, Z., Samcovic, A.: Face Detection Approach In Neural Network Based Method For Video Surveillance. In: NEUREL 2006. IEEE (2006)Google Scholar