Neural network technology for pattern recognition, stock prediction and market forecasting


Pattern recognition

DCT-ANN Face Identification

Wavelet-ANN Face Recognition

Text-Independent Speaker Recognition based on ANN

Assembler-based Neural Network Simulator

Facial Expression Recognition System

Iris Recognition Based on Neural Networks

Neural Networks Based Signature Recognition

Eye Detection Based Facial Expression Recognition

Gait Recognition System

Leaf Recognition System

Optical Character Recognition

Neural Network Fingerprint Recognition

Keystroke Recognition

EEG Recognition

Neural Network Speech Recognition

Image processing

Image Compression With Neural Networks

Stock Market Forecasting

Neural Network Forecasting

External resources

Advanced Source Code .Com

Genetic Algorithms .It

Face Recognition .It

Iris Recognition .It

Robust Eye Detection

Download now Matlab source code
Requirements: Matlab, Matlab Image Processing Toolbox, Matlab Neural Network Toolbox.

Facial expressions convey non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of facial expressions can be an important component of natural human-machine interfaces; it may also be used in behavioural science and in clinical practice. Although humans recognise facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenge. We have developed a fast and efficient algorithm for facial expression recognition. The algorithm consists of three main stages: eye region locating stage, the eye detection stage and feature vectors extraction. In the first stage, an effective approach to fast location of the eye region is developed. In the second stage, eye edge contour searching directed by knowledge is introduced in detail. Regional image processing techniques are also described in the second stage. The main purpose of the first stage is to locate the eye region roughly. The algorithm employed in the second stage is restricted to application in just this region. It reduces the complexity of the first stage and improves the reliability in the second stage. Expression representation can be sensitive to translation, scaling, and rotation of the head in an image. To combat the effect of these unwanted transformations, the facial image may be geometrically standardised prior to classification. This normalisation is based on references provided by the eyes. Once eye regions has been detected, in the third stage an invariant coordinate system is generated and extracted feature vectors are used to train a neural network. This code has been tested using the JAFFE Database, available at Using 150 images randomly selected for training and 63 images for testing, without any overlapping, we obtain a recognition rate equal to 83.14%. The semantic data ratings for this database are available at

Index Terms: Matlab, source, code, facial, expression, recognition, JAFFE, neural networks, network, eye, eyes, detection.

Release 1.0 Date 2009.02.18
Major features:

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