Abstract: This paper proposes new hybrid approaches for face
recognition. Gabor wavelets representation of face images is an
effective approach for both facial action recognition and face
identification. Perform dimensionality reduction and linear
discriminate analysis on the down sampled Gabor wavelet faces can
increase the discriminate ability. Nearest feature space is extended to
various similarity measures. In our experiments, proposed Gabor
wavelet faces combined with extended neural net feature space
classifier shows very good performance, which can achieve 93 %
maximum correct recognition rate on ORL data set without any preprocessing
step.
Abstract: We introduce an algorithm based on the
morphological shared-weight neural network. Being nonlinear and
translation-invariant, the MSNN can be used to create better
generalization during face recognition. Feature extraction is
performed on grayscale images using hit-miss transforms that are
independent of gray-level shifts. The output is then learned by
interacting with the classification process. The feature extraction and
classification networks are trained together, allowing the MSNN to
simultaneously learn feature extraction and classification for a face.
For evaluation, we test for robustness under variations in gray levels
and noise while varying the network-s configuration to optimize
recognition efficiency and processing time. Results show that the
MSNN performs better for grayscale image pattern classification
than ordinary neural networks.
Abstract: In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems.