Free photo identification software for windows

Phase-Based Iris Identification 1 offers an advanced and proper way of iris recognition today that is to generate feature vectors corresponding to individual iris images and to perform iris matching based on some distance metrics.

One of the difficult problems in feature-based iris recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. An efficient algorithm is presented for iris recognition using phase-based image matching.

Requirements:

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

Intel Chipset Identification Utility - easy way to identify the Intel chipset that is located on your motherboard The Intel Chipset Identification Utility gives you an easy way to identify the specific Intel chipset that is located on your motherboard. This information can be used to determine if your system supports the Intel Application Accelerator.
Simply download the file and run it. The utility is a standalone program with no installer package so it will not create any shortcuts under the Start/Programs folder of your operating system.
The utility can be left on your system if desired or deleted after it has been run. The Intel Chipset Identification Utility it should also fit on a floppy diskette if desired.
If certain Intel chipset components are not detected by the Intel Chipset Identification Utility, you may have the ability to update the version from within the utility.
When you try updating the version of the Intel Chipset Identification Utility, it will check an Intel web site (if Internet connection is detected) to determine if a newer version is available.

Fourier Bessel Transform For Face Identification 1 offers you a convenient novel biologically motivated face recognition algorithm based on polar frequency. Fourier Bessel Transform For Face Identification - Matlab Source Code. Polar frequency descriptors are extracted from face images by Fourier-Bessel transform (FBT). Most of the current face recognition algorithms are based on feature extraction from a Cartesian perspective, typical to most analog and digital imaging systems

The primate visual system, on the other hand, is known to process visual stimuli logarithmically. An alternative representation of an image in the polar frequency domain is the two-dimensional Fourier-Bessel Transform. This transform found several applications in analyzing patterns in a circular domain, but was seldom exploited for image recognition. Matlab Image Processing Toolbox is required.

Neural Networks - DCT for Face Identification 1 is developed to be a useful and essential source code for Matlab. High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. Discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth.

It is demonstrated experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.

Requirements: Matlab Image Processing Toolbox and Matlab Neural Network Toolbox