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

Neural Networks For Non-Intrusive Biometric Recognition

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

Gait recognition is the process of identifying an individual by the manor in which they walk. This is a marker less unobtrusive biometric, which offers the possibility to identify people at a distance, without any interaction or co-operation from the subject, this is the property which makes it so attractive as a method of identification.

The interest in gait as a biometric is strongly motivated by the need for an automated recognition system for visual surveillance and monitoring applications. Recently, the use of gait for people identification in surveillance applications has attracted researchers from computer vision. The suitability of gait recognition for surveillance systems emerges from the fact that gait can be perceived from a distance as well as its non-invasive nature. Although gait recognition is still a new biometric, it overcomes most of the limitations that other biometrics suffer from such as face, fingerprints and iris recognition which can be obscured in most situations where serious crimes are involved.

We have developed a fast and accurate algorithm for gait recognition based on spatio-temporal statistical distribution of pixels present in silhouettes images. Code has been tested on CASIA Gait Database. Input frames are processed and extracted feature vectors are used to train a neural network.

Index Terms: Matlab, source, code, gait, recognition, identification, matching, silhouettes, neural, networks.

Release 1.0 Date 2009.07.22
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