Free speech recognition system software for windows

Speech Recognition System 1 is a utility used to transform words into electronic text or commands. It is for speech recognition. Available on request protected P-files for performance evaluation.Speech recognition provides the possibility of converting words pronounced by human into electronic text or commands. Matlab and Matlab Signal Processing Toolbox are required. The package includes Windows MEX files.

Iris Recognition System 1 is created as a professional mathematics related tool. Iris Recognition System Matlab Source Code. The iris of each eye is unique. No two irises are alike in their mathematical detail--even between identical twins and triplets or between one's own left and right eyes. Unlike the retina, however, it is clearly visible from a distance, allowing easy image acquisition without intrusion.

The iris remains stable throughout one's lifetime, barring rare disease or trauma. The random patterns of the iris are the equivalent of a complex human barcode, created by a tangled meshwork of connective tissue and other visible features. The iris recognition process begins with video-based image acquisition that locates the eye and iris. Matlab Image Processing Toolbox is required.

Palmprint Recognition System 1 comes as a practical and high quality utility for palmprint recognition. Palmprint Recognition System - Matlab Source Code. In the field of biometrics, palmprint is a novel but promising technology. Limited work has been reported on palmprint identification and verification, despite the importance of palmprint features.

There are many unique features in a palmprint image that can be used for personal identification. Principal lines, wrinkles, ridges, minutiae points, singular points, and texture are regarded as useful features for palmprint representation.

Requirements:

• Windows 95/98/Me/NT/2000/XP/2003 Server/Vista
• Matlab 6.5

Fingerprint Recognition System is a free tool that will recognize and verify fingerprints.
With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically.
The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode.
The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.
Index Terms: Biometrics, FingerCode, fingerprints, flow pattern, Gabor filters, matching, texture, verification.
The localization of core point represents the most critical step of the whole process. A good matching requires an accurate positioning, so the small errors must also be avoided. The usage of complex filtering techniques, can greatly improve accuracy. On the other side, for very poor quality input images, a traditional algorithm can fail even using a hierarchical approach with a multiscale filtering.
Main features:
- An improved algorithm used in fingerprint matching, highly recommended for high-performance applications
- A generalized and optimized version of the algorithm for core point localization
- Improved database
- Demo code (protected P-files) available for performance evaluation
- 5.0 2005.05.15 The merging technique is greatly improved. This new algorithm was tested on FVC2004 training fingerprint images. Test results are available on request. Please email me in order to obtain them.
- Parameters for image segmentation are estimated automatically
- An improved algorithm is used when the rotated FingerCode is added to database. Now this procedure does not introduce any additional noise
- A faster fingerprint image acquisition when a new fingerprint image is added to database
- Implementation of 1D and 2D recursive Gabor filtering
- Optimized pixel-wise orientation field estimation ( 80% faster than Release 4.0 )
- List of fingerprint databases available on the web
- 4.0 2005.03.21 An improved algorithm for core point detection, based on a novel hybrid technique
- A better fingerprint segmentation which makes use of morphological operations (binary erosion and dilation)
- Exhaustive documentation of the implemented algorithms
- Improved GUI
- Better error management
- Image enhancement
- Orientation field estimation
- 3.0 2004.06.22 Major bugs fixed
- New GUI
- Complex filtering techniques
- Improved core point determination
- Robustness against noise
- Modifiable simulation parameters
- 2.0 2003.11.29 New GUI
- 8 Gabor filters 0 22.5 45 67.5 90 112.5 135 157.5 degrees
- Convolution is performed in frequency domain
- DataBase
- Fingerprint matching
- Error management

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