• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.367-371
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• 2006

Multilayer percepteon has the advantage of learning their optimal parameters and efficiency. However, MLP shows some drawbacks when dealing with high dimensional data within the input space. Also, it Is very difficult to find the optimal parameters when the input data are highly correlated such as large scale face dataset. In this paper, we propose a novel technique for face recognition based on LDA and local MLP. To resolve the main drawback of MLP, we calculate the reduced features by LDA in advance. And then, we construct a local MLP per group consisting of subset of facedatabase to find its optimal learning parameters rather than using whole faces. Finally, we designed the face recognition system combined with the local MLPs. From various experiments, we obtained better classification performance in comparison with the results produced by conventional methods such as PCA and LDA.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.266-274
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• 2003

Distance Fourier descriptors of local target boundary and feature information fusion using multiple MLPs (Multilayer perceptrons) are proposed. They are used to identify nonoccluded and partially occluded targets in natural FLIR (forward-looking infrared) images. After segmenting a target, radial Fourier descriptors as global shape features are defined from the target boundary. A target boundary is partitioned into four local boundaries to extract local shape features. In a local boundary, a distance function is defined from boundary points and a line between two extreme points. Distance Fourier descriptors as local shape features are defined by using distance function. One global feature vector and four local feature vectors are used as input data for multiple MLPs to determine final identification result of the target. In the experiments, we show that the proposed method is superior to the traditional feature sets with respect to the identification performance.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.700-702
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• 2004

A local-step optimization method is proposed to supplement the global-step optimization methods which adopt online update mode of internal weights and error energy as stop criterion in learning of multilayer perceptrons (MLPs). This optimization method is applied to the standard online error backpropagation(EBP) and the performance is evaluated for a speaker verification system.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.568-570
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• 2004

The error-backpropagation (EBP) algerithm for training multilayer perceptrons (MLPs) is known to have good features of robustness and economical efficiency. However, the algorithm has difficulty in selecting an optimal constant learning rate and thus results in non-optimal learning speed and inflexible operation for working data. This paper Introduces an elastic learning rate that guarantees convergence of learning and its local realization by online upoate of MLP parameters Into the original EBP algorithm in order to complement the non-optimality. The results of experiments on a speaker verification system with Korean speech database are presented and discussed to demonstrate the performance improvement of the proposed method in terms of learning speed and flexibility fer working data of the original EBP algorithm.