Free biometric handpunch software for windows

WareSeeker Editor

PayPunch is a Biometric Time Clock & Attendance tracking system that replaces the typical time clock used to track employees attendance. Instead of employees punching a time-card, they simply insert their hand into the reader and enter their employee number. PayPunch does the rest! PayPunch ROI is absolutely outstanding and it can pay for itself in as little as 2-3 months. How will it save your company money? PayPunch is a simple solution that pays for itself over and over again by allowing your company to:
Automate payroll processing
Eliminate buddy-punching
Eliminate time-cards
Eliminate early punch-ins
Eliminate unauthorized overtime
Eliminate hours of payroll calculations
Accurately reports employee time/attendance to Payroll & HR
PayPunch Benefits:
Drastically Reduces Payroll Processing Cost
Eliminates paper cards and all cost associated with paper cards (cards, ribbons, etc.)
Reduces time needed to verify attendance data
Reduces time needed to calculate and process payroll
Eliminates mistakes in calculating work hours and other human errors (manual reentering, etc.)
Centralized payroll processing reduces HR and accounting personnel involvement
Eliminates "Buddy-Punching"
Biometric HandReaders provide with highest level of security
Impossible to punch in or out for somebody else
Helps Managers and Supervisors Control Overtime
Enforces company rounding rules and policies for multiple shifts and departments
Provides reports identifying overtime usage for better control
Keeps historical data to analyze trends
.. and much more!

SmartWallet 1.1 is developed as a single login system that enabled you to access to multiple web accounts with master password. The master password is uniquely protected with our prorietary BioSignIN Engine. BioSingIN is an additional layer of security that strengthens traditional password-based authentication system with typing biometric technique. You can login into multiple Web accounts with single master password such as gmail.

Enhancements: Enhances login password security.

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