Free widi recognition system professional 3.3 software for windows

Iris Recognition System is a free software that can locate and identify the eye and iris.
The iris of each eye is unique. No two irises are alike in their mathematical detail--even between identical twins and triplets or between ones 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 ones 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.
The boundaries of the pupil and iris are defined, eyelid occlusion and specular reflection are discounted, and quality of image is determined for processing. The iris pattern is processed and encoded into a record (or "template"), which is stored and used for recognition when a live iris is presented for comparison. Half of the information in the record digitally describes the features of the iris, the other half of the record controls the comparison, eliminating specular reflection, eyelid droop, eyelashes, etc.
A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favourable conditions, and there have been no independent trials of the technology.
The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localise the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalised into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantised to four levels to encode the unique pattern of the iris into a bit-wise biometric template.
The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed. The system performed with perfect recognition on a set of 75 eye images; however, tests on another set of 624 images resulted in false accept and false reject rates of 0.005% and 0.238% respectively. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.
Main features:
- Highly optimized code: the execution time reduced of 94%, more than 16 times faster than original code (Libor Maseks implementation)
- Matching module
- Optimized memory allocation
- Iris recognition
- Interactive and intuitive GUI
- C code included

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

Speaker recognition is a tool to automatically recognizing who is speaking on the basis of individual information included in speech waves.
This technique makes it possible to use the speakers voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.
Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments).
The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients.
A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients. Therefore it provides a stabler representation from one repetition to another of a particular speakers utterances.
As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

AIDA32 is a professional system information, diagnostics and benchmarking program for Windows platforms. It extracts details of all components of the PC. It can display information on the screen, print it, or save it to file in various formats like HTML, CSV or XML. For corporate users, AIDA32 Enterprise offers command-line switches, network audit and audit statistics, remote system information and network management.

Apnajhelum Professional 2002 is designed as a smart and useful utility to chat on IRC. Basically it's for DALNET irc NETWORKS. but it works on almost all SERVERS of IRC. No bugs at the time of release and best of all is that power and entertainment united.