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Overview
Like fingerprint recognition, human face-based biometrical identification is
becoming increasingly popular. Facial recognition is used in systems that
control access to physical locations, computer/network resources, bank accounts,
or register employee attendance time in enterprises.
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Many of these applications
can run on a PC. However some applications require that the system be
implemented on low cost, compact and/or mobile embedded devices such as cell
phones, handheld PCs, door or gate locks, etc.
In comparison with fingerprints, facial recognition in embedded devices can
be even more practical in many situations and more comfortable for the user
because no physical contact with the device is required. PDAs, smart phones and
other compact devices with integrated cameras and the ability to add custom
software are available in the market, enabling the implementation of embedded
facial recognition technology without additional hardware development.
The facial recognition algorithm for embedded systems requires some specific
features in comparison with PC-based face identification. Embedded or handheld
devices usually have weaker processors than personal computers. The PC-based
face image detection and template extraction software is computationally
expensive; therefore substantial algorithm modification is required to achieve
acceptable template extraction time on the embedded device.
Having the ability to create a mixed embedded/PC and/or multi-biometric
face/fingerprint identification system by integrating several technologies is an
important advantage. Using a combination of biometric technologies allows
implementation of systems with higher levels of security and reliability, as
well as achieving higher matching speeds even when using very large databases.
The technology
Neurotechnology offers the
FaceCell embedded facial recognition technology, developed on the VeriLook
basis, but having about 3 times faster image processing and feature extraction
algorithm. The FaceCell includes these proprietary algorithmic solutions:
- multiple face localization in live video streams and still images,
- simultaneous multiple face processing and identification in single
frame,
- identification (1:N) ability with matching speed of 3,000 faces per
second,
- features generalization for even more reliable identification,
- compact template (2.3 Kbytes) that allows to handle large databases.
Algorithm demo application is available for
downloading.
Read more about
the technology.
The EDK (Embedded Development Kit)
FaceCell 1.1 EDK is based on the FaceCell technology, and is intended for
embedded biometric systems developers and integrators. The EDK includes
libraries for major operating systems and embedded platforms, and programming
sample applications with source codes. The FaceCell ANSI C source code package
could be also obtained to port the software to another platforms.
The following types of EDK are available:
- FaceCell 1.1 Library EDK is intended for biometric
system projects using hardware based on ARM processors. You can
download
trial version of FaceCell 1.1 Library EDK to try it on your hardware.
Read
more…
- FaceCell 1.1 source code EDK is intended for large
biometric system projects using third party or custom hardware. It includes
FaceCell 1.1 source code, samples and documentation for MS Windows CE and
Linux.
Read
more…
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