widi recognition system pro v3.22 build 579
Sponsored Links
Sponsored Links
Secleted [ 0 ] software to compare
Results 1 - 15 of about 36976
Iris Recognition System 1.0
Iris Recognition System is a free software that can locate and identify the eye and iris more>>
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
<<lessThe 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
Download (54.5KB)
Added: 2006-09-03 License: Freeware Price:
1364 downloads
Other version of Iris Recognition System
Iris Recognition System Matlab Source Code. The iris of each eye is ...Iris Recognition System 1 is created as a professional mathematicsLicense:Freeware
Writer Recognition System 1
Writer Recognition System provides you with a sophisticated product that research in automatic writer identification has primarily focused on the statistical approach. more>> <<less
Download (22.7KB)
Added: 2007-10-10 License: Freeware Price:
downloads
Face Recognition System 2.0
Face Recognition System. Matlab source code for face recognition. Eigenfaces for face recognition. Index terms: Face recognition, face matching, eigenfaces, eigenvalues, face verification, matlab code more>>
Face Recognition System. Matlab source code for face recognition. Eigenfaces for face recognition. Research on automatic face recognition in images has rapidly developed into several inter-related lines, and this research has both lead to and been driven by a disparate and expanding set of commercial applications. The large number of research activities is evident in the growing number of scientific communications published on subjects related to face processing and recognition. Index terms: Face recognition, face matching, eigenfaces, eigenvalues, face verification, matlab code, eigenvectors, Karhunen-Loeve algorithm.
Requirements: Matlab, Matlab Image Processing Toolbox
<<less Download (0.56MB)
Added: 2008-09-07 License: Freeware Price: $0
810 downloads
Fingerprint Recognition System 5.1
Fingerprint Recognition System is a free tool that will recognize and verify fingerprints more>>
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
<<lessWith 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
Download (512KB)
Added: 2006-09-02 License: Freeware Price:
1472 downloads
Palmprint Recognition System 1
Palmprint Recognition System comes as a practical and high quality utility for palmprint recognition. more>>
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
Download (150.4KB)
Added: 2007-10-10 License: Freeware Price:
downloads
Speech Recognition System 1.0
Requirements: Matlab, Matlab Signal Processing more>>
Requirements: Matlab, Matlab Signal Processing Toolbox
<<less Download (28.32KB)
Added: 2008-09-07 License: Freeware Price: $0
456 downloads
Other version of Speech Recognition System
Transform words pronounced by human into electronic text or ...Speech Recognition System 1 is a utility used to transform wordsLicense:Freeware
Speaker Recognition System 1.0
Speaker recognition is a tool to automatically recognizing who is speaking on the basis of individual information more>>
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.
<<lessThis 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.
Download (598KB)
Added: 2006-07-26 License: Freeware Price:
1452 downloads
Other version of Speaker Recognition System
Speaker recognition is the process of automatically ...Speaker recognition is the process of automatically recognizingLicense:Freeware
Paragon System Backup 2010 Build 8552 RC
Paragon System Backup 2010 Build 8552 RC provides you with a powerful and easy-to-use software which is designed to provide simple and automatic entire system & data backup. more>> <<less
Download (76.2MB)
Added: 2009-07-13 License: Freeware Price: FREE
1 downloads
Facial Expression Recognition System 1
Analyze facial expressions. more>>
Facial Expression Recognition System 1 is a powerful tool that you propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications.
Major Features:
- Facial Expression Recognition based on Neural Networks
- PCA-based dimensionality reduction
- High recognition rate
- Intuitive GUI
- Easy C/C++ implementations
- Demo code (protected P-files) available for performance evaluation
WareSeeker Editor
<<less
Download (412.3KB)
Added: 2007-03-22 License: Freeware Price:
120 downloads
Intrusion detection system - Sax2 Free 3.00 Build 1.0.0.84
PLEASE REVIEW AND EDIT CAREFULLY A professional intrusion detection and response system more>> <<less
Download (5.3MB)
Added: 2009-06-07 License: Freeware Price: FREE
1 downloads
Efficient Notes Free 1.75 Build 56
Efficient Notes Free 1.75 Build 56 is a powerful software that is packaged all-in-one to help you manage your notes or memories. more>> <<less
Download (2.9MB)
Added: 2009-09-02 License: Freeware Price: Notavailable
downloads
Hybrid Iris Recognition 1.0
Hybrid Iris Recognition is an advise mathematic program for Windows. The use of biometric signatures, instead of tokens such as identification cards or computer passwords, more>>
Hybrid Iris Recognition 1.0 is an advise mathematic program for Windows. The use of biometric signatures, instead of tokens such as identification cards or computer passwords, continues to gain increasing attention as an efficient means of identification and verification of individuals for controlling access to secured areas, materials, or systems and a wide variety of biometrics has been considered over the years in support of these challenges. Iris recognition is especially attractive due to the stability of the iris texture patterns with age and health conditions. Iris image segmentation and localisation is a key step in iris recognition and plays an essential role the accuracy of matching.
Download (484.5KB)
Added: 2009-05-12 License: Freeware Price:
downloads
Font Image Recognition Routine 1.0.1
Font Image Recognition Routine is a freehand drawn character graphics recognition routine more>>
Font Image Recognition Routine will allow you to build vector file out of 100x100 pixels 24-bit color depth bitmap image of freehand drawn fonts.
Comparison of two different vector files states same/different font drawing. This routine is aimed help PDA users write/input text in an easier way.
This routine creates a vector shape sequence file (using -i switch) out of an 100x100 pixels 24 bit colour depth black and white image representing a character truetype image (font) or character freehand drawing.
Then using the -c switch the two index files derived from two different images are compared and graphics matching result is displayed.
At present the routine builds shape vectors on black/white bitmaps, it does not support different resolution nor colors/color depth. This development is aimed for PDA users using easier ways for text input.
<<lessComparison of two different vector files states same/different font drawing. This routine is aimed help PDA users write/input text in an easier way.
This routine creates a vector shape sequence file (using -i switch) out of an 100x100 pixels 24 bit colour depth black and white image representing a character truetype image (font) or character freehand drawing.
Then using the -c switch the two index files derived from two different images are compared and graphics matching result is displayed.
At present the routine builds shape vectors on black/white bitmaps, it does not support different resolution nor colors/color depth. This development is aimed for PDA users using easier ways for text input.
Download (0.1MB)
Added: 2007-04-27 License: Freeware Price:
940 downloads
Winamp 5.55 Build 2405
Winamp is more than just a player. Its your window to the multimedia world. From MP3s to streaming video, Winamp is the one place you go to feed your audio/video habit. more>> <<less
Download (13.00MB)
Added: 2009-02-27 License: Freeware Price:
843872 downloads
BlogTrader Platform 1.0.2 build 2052
BlogTrader Platform is an open source stock technical analysis tool built on pure Java. Humai Trader will also be a platform for custom indicators and charts more>> <<less
Download (6.1MB)
Added: 2006-04-08 License: Freeware Price:
83 downloads
Secleted [ 0 ] software to compare
Copyright Notice:
Software piracy is theft, Using crack, password, serial numbers, registration codes, key generators is illegal and prevent future software development. The above widi recognition system pro v3.22 build 579 search only lists software in full, demo and trial versions for free download. Download links are directly from our mirror sites or publisher sites, torrent files or links from rapidshare.com, yousendit.com or megaupload.com are not allowed
