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Raspina is a company that works on biometric software solutions and product provider.
Our company works on biometric technologies such as iris, face and fingerprint recognition. We have developed and produced a whole range of biometric access control, traffic control and verification and identification systems to our customers. We are committed to providing the highest quality products and services with unparalleled innovation, high accuracy and speed.
We have developed and produced a whole range of biometric access control, traffic control, verification and identification systems to our customer.


Raspina is a company that works on biometric software solutions and product provider. Our company works on biometric technologies such as iris, face and fingerprint recognition. We have developed and produced a whole range of biometric access control, traffic control and verification and identification systems to our customers. We are committed to providing the highest quality products and services with unparalleled innovation, high accuracy and speed.

Overview of Face recognition

In general, human identification systems using a wide range of information that our senses provide. This information separately or together, are used for remembering and recognition. In addition to environmental information also play an important role in human identification. For example, identify the host of a television program in the program is much easier to recognize him in the street or any place else. Each person's face as a very good indicator to identify him because Take pictures of the person can be done easily. That’s why face recognition is an important topic in applications such as security systems, control, credit cards and etc. Developed a computational model for face recognition is quite difficult because of the complexity of multi-dimensional visual appearance and structure. There are a multitude of differing approaches to solving this complex problem. And while much progress has been made many challenges remain.

Facial recognition system!

Automated facial recognition involves the identification of an individual based on his or her facial geometry. For facial recognition to be successful, there needs to be a quality digital image of an individual’s face, a database of digital images of identified individuals, and facial recognition software that will find a match between the two. Faces have been transformed into electronic information that can be aggregated, analyzed and categorized in unprecedented ways. What makes facial image data so valuable, and so sensitive, is that it is a uniquely measurable characteristic of our body and a key to our identity. Facial recognition technology aims to identify or authenticate individuals by comparing their face against a database of known faces and looking for a match. The process can be broken down into 3 very general steps.
First, the computer must find the face in the image. It then creates a numeric representation of the face based on the relative position, size and shape of facial features. Finally, this numeric “map” of the face in the image is compared to database images of identified faces, for example, a driver’s license database. Identification is often the goal of public safety and national security applications, such as identifying individuals during a riot, or maintaining the security of high traffic public places such as airports and sports arenas. Facial recognition is highly suitable for identification applications because facial images can be captured at a distance and without the individual’s knowledge. Other biometrics, like gait or voice recognition, can also be used for identification from a distance and without consent, but they have obvious limitations that render them less useful. 

Overview Raspina facial recognition software 

*** Recognition based on neural network technology.
*** Short processing time, high recognition rate.
*** Recognition regardless of vantage point and facial changes.
*** Reliable matching.
*** Identification and authentication based on individual facial features.
*** Easy adaptation to existing IT systems.
*** Flexible integration into many types of video monitoring systems.
*** Supporting diverse graphic and video formats as well as live cameras.
*** Fast, accurate identification of persons of interest without physical contact.
*** Enables law enforcement to take advantage of the growing volume of photos and video footage to solve crimes.
*** Enrolment from photograph / still camera / video stream.
*** Real time multiple video stream analysis and reconcile the evidence with facts.
*** N: N matching from the video / database.
*** 1: N matching.
*** High scalability in terms of watch list size, number of cameras and live video stream.
*** Comprehensive audit trail reports.
*** Using native algorithms.
*** Using native database.

Some of facial recognition used cases 

The day after the June 2011 hockey riot in Vancouver, the Insurance Corporation of British Columbia (ICBC) offered to help police identify rioters by running facial recognition software on images from the riot and comparing suspects to images in its drivers licence database. The BC Privacy Commissioner ruled that while ICBC can use the technology to detect and prevent driver's licence fraud, the corporation cannot use its database to help police identify riot suspects because this is a different purpose, of which customers were not notified.
These events led to the tabling of a federal Private Members Bill, Bill 309, An Act to Amend the Criminal Code (Concealment of Identity) that proposed to criminalize the wearing of masks during unlawful protests. Concerns have been expressed that the new law (which received Royal Assent June 19, 2013) would create a chill on protests, including peaceful ones.
In the U.S., FBI’s Next Generation Identification Program (NGI) uses a variety of biometrics, including facial recognition, to identify and monitor “persons of interest.” For the facial recognition component of the program, which is expected to be fully operational in summer 2014, the FBI will integrate its own databases of searchable photos with those at the state level. The resulting database will contain the biometric and biographical information of over 100 million Americans; will be integrated with the extensive networks of CCTV cameras that already monitor public and commercial spaces, such as streets,
parking lots, airports, banks, and shopping malls; and will be made available to different levels of government.

Sporting events are seen by many as potential targets for terrorism. Surveillance cameras can be used to recognize potential terrorists. Football's the Superbowl, where video analytics such as facial recognition have been used to scour the crowds for known felons, is an example.

Facial detection and recognition systems are also being used for security purposes in the offline world. Gadspot, mentioned earlier, manufactures inexpensive facial recognition enabled security cameras. An Ottawa company, iWatchLife, sells smart surveillance cameras that can alert home and business owners to certain events, such as if the number of individuals on the premises exceeds a set amount. The company is working to add a facial recognition component that could be used for features such as access control, for example, not allowing a service person to enter a home office.

When we talk about surveillance, we tend to concentrate on the problems of data collection: CCTV cameras, tagged photos, purchasing habits, our writings on sites like Facebook and Twitter. We think much less about data analysis. But effective and pervasive surveillance is just as much about analysis. It's sustained by a combination of cheap and ubiquitous cameras, tagged photo databases, commercial databases of our actions that reveal our habits and personalities, and most of all fast and accurate face recognition software.

The face recognition approach possesses "invariant" recognition characteristics, including face recognition where facial expressions, viewing perspectives, three-dimensional poses, individual appearance, and lighting vary and occluding structures are present.
This improvement ensures that security personnel can effectively and at low cost use the Internet to remotely control a mobile robot to track and identify a potential intruder.

As part of the perimeter security initiative, Canada and the U.S. have discussed using face recognition scanners linked to image databases in both countries. The purpose would be to identify wanted individuals or convicted criminals. In Australia, facial recognition is used in conjunction with fingerprinting at borders to identify fraudulent visa applicants. Australian immigration officials are also using facial recognition in efforts to address visa fraud
and illegal workers. This is part of a national campaign to crack down on identity theft and the use of false identities to facilitate crime. Japan is testing automated immigration gates at its major airports whereby the faces of inbound and outbound travelers are automatically compared to their passport pictures. This is intended to speed traveler
traffic through immigration gates.  

Authenticating computer users grows more challenging as fraudsters attempt to break into computer systems to steal valuable information, fraudulently drain bank accounts, and maliciously damage computer systems. How can Information Technology organizations possibly combat this escalating threat? To date, the answer has generally been requiring more complex passwords. But that in turn results in more onerous processes and
difficulty for the normal user while not necessarily making computer-generated attacks less effective source.

The ideal solution will achieve higher security while at the same time reducing the complexity of the authentication process for the normal user. The current attempt at achieving a more ideal solution consists of Two-Factor Authentication (“2FA”) and Two-Step
Authentication systems, explained in greater detail below. These are coming into common use at a consumer level.

Although superior to a single factor, the two-factor approaches nevertheless are vulnerable, especially to insider fraud. The solution in the coming years goes beyond today’s two-factor and two-step solutions, more generally referred to as Multi-Factor Authentication, or “MFA” for short. The remainder of this document provides background information on current approaches and then discusses a strategy for robust MFA solutions
that impose minimal impedance to the login process for normal users.

A US company is marketing facial recognition software that can be integrated with ATMs and retail point-of-sale terminals to provide secure authentication. The software is designed to work with existing security cameras.
An Italian company developed the EyeSee mannequin that uses a camera disguised as an eye to collect data such as age, gender and race about passing shoppers. While to date, only face detection technology is being used, facial recognition applications may not be far off. Also in the retail sector, NEC has launched a facial recognition service that collects not only demographic data about shoppers, but also their shopping habits, such as frequency and timing of their visits. 

Logical access controlLogical access control is a major area of application for biometric technology. When we  say, “It’s time to kill the password,” this is the tech we’re talking about. Whether it’s securing the apps on yours martphone, gaining access to a work email or enabling an effective BYOD policy, biometric logical access control solutions can launch you into the next generation of convenience and cyber protection.
There is an ever growing selection of biometric logical access control options, especially in the consumer markets, and the vendors below are a great place to start your search for better authentication options.

Micro expressions reveal your deepest emotions, even when you are trying to hide them. Now a machine vision algorithm has learned to spot them, with wide-ranging applications from law enforcement to psychological analysis. 

The U.S. Navy has reportedly been using Robocop-style glasses, which are fitted with a small camera that sees as far as 19 km. The glasses can capture 400 images a second and compare them with a central computer database of 13 million faces.
Technology being considered by the U.S. military consists of a camera integrated with a soldier’s optics for his weapon, together with a portable database that could contain over a million images. This equipment would allow soldiers to identify terrorists and other enemies in seconds in the field without requiring any bandwidth.
Another potential military application involves robots equipped with facial recognition software that would be sent into the field to retrieve wounded soldiers.

Authenticating authorized persons with face recognition supports security measures in airports, stadiums, office spaces, manufacturing sites and other buildings. An example of a 1:1 verification application, the image captured by a camera is compared to the image stored for the authorized person. The same method can be used for access to airplanes, ferries, cruise ships, etc. 

Contact US


5th Floor, No. 16, Gole Yakh Gharbi Alley, Boulevard Ayneh, St. Shahid Amir Pabarja, Shariati Street, Tehran, Iran.


Phone: +98 21 22 00 80 90 , +98 21 22 00 82 51 
Fax: +98 21 22 00 80 91

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