VeriFinger

FingerCell

MegaMatcher

VeriLook

FaceCell

Template Conversion

SmartCard Finger-Match

SmartCard Face-Match

VeriEye

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. 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…

Why FaceCell?

FaceCell algorithm is designed for embedded biometric systems developers. The algorithm has certain capabilities:

Algorithm 

The FaceCell algorithm is similar to the VeriLook algorithm and includes these features:
• Fast and accurate face localization for reliable detection of multiple faces in the images.
• Simultaneous multiple face processing and identification in a single frame. All faces in the current frame are detected in about 1 second* and then each face template is extracted in about 1 second*.
• Face quality threshold can be used during face enrollment to ensure that only the best quality face template will be stored into database.
• The FaceCell face template matching algorithm compares 3,000 faces per second*.
• Applications implemented using FaceCell EDK can handle large face databases, as one facial feature template is only 2.3 Kbytes.
• Features generalization mode generates the collection of the generalized face features from several images of the same subject. Then each face image is processed, features are extracted, and the collections of features are analyzed and combined into a single generalized features collection which is written to the database. This way, the enrolled feature template is more reliable and the face recognition quality increases considerably.

* All performance evaluations were performed using a HP iPAQ Pocket PC with XScale PXA270 processor running at 416 MHz

Specifications

All performance evaluations were performed using a HP iPAQ Pocket PC with XScale PXA270 processor running at 416 MHz

Algorithm's demo

EDK Overview

FaceCell Embedded Development Kit is based on the FaceCell embedded facial recognition algorithm that is designed to be used in handheld devices with embedded cameras, such as PDAs or smart phones. FaceCell EDK includes libraries for Linux and Microsoft Windows Mobile on the ARM platform.

It is possible to use FaceCell technology on other platforms and with other operating systems by obtaining the FaceCell source code EDK. The FaceCell algorithm source code is written in ANSI C, thus it is easily portable.

These types of FaceCell 1.1 EDK are available:
• FaceCell 1.1 Library EDK
• FaceCell 1.1 source code EDK

FaceCell 1.1 Library EDK

FaceCell 1.1 Library EDK is intended for development projects using hardware based on ARM processors.

FaceCell 1.1 Library EDK includes these components:
• MS Windows Mobile components
   ° FaceCell 1.1 library (for Microsoft Visual Studio 2005 with SP1).
   ° FaceCell programming sample in Visual C++ 2005 SP1.
   ° FaceCell tutorials in C.
• ARM Linux components
   ° FaceCell 1.1 library (for ARM-Linux GCC C compiler).
   ° FaceCell tutorials in C.
• Documentation.

System requirements for FaceCell 1.1 Library EDK
• ARM-based 400 MHz processor is recommended for face enrollment in less than two seconds. Supported ARM processor core families are: XScale, StrongArm, ARM11, ARM10, ARM9.
• At least 8 Mb of memory for FaceCell code and data arrays.
• ARM Linux (glibc 2.3.4 or later) or Microsoft WindowsMobile 2003 (or later) operating system
• (Optional) Embedded camera with at least 320 x 240 pixels physical resolution (640 x 480 pixels recommended). The device must be running MS Windows Mobile 5 to use the embedded camera with the demo application.

FaceCell 1.1 source code EDK

FaceCell 1.1 source code EDK is intended for developers who are going to integrate facial recognition technology into a custom device.

FaceCell 1.1 source code EDK contains these components:
• FaceCell 1.1 source code
   • Project for GCC compiler (ARM-Linux platform)
   • Project for Microsoft Visual Studio 2005 (Pocket PC 2003 and Pocket PC 2005* platforms)
• FaceCell 1.1 Algorithm and Source Code Description
• Sample applications:
   • Project for GCC compiler (ARM-Linux platform)
   • Project for Microsoft Visual Studio 2005 (Pocket PC 2003 and Pocket PC 2005* platforms)
• FaceCell EDK developer's guide
* Pocket PC 2005 development requires Windows Mobile 5.0 SDK for Pocket PC.

System requirements:
• ARM-based 400 MHz processor is recommended for face enrollment in less than two seconds. Supported ARM processor core families are: XScale, StrongArm, ARM11, ARM10, ARM9.
• At least 8 Mb of memory for FaceCell code and data arrays.
• ARM Linux (glibc 2.3.4 or later) or Microsoft WindowsMobile 2003 (or later) operating system
• (Optional) Embedded camera with at least 320 x 240 pixels physical resolution (640 x 480 pixels recommended). The device must be running MS Windows Mobile 5 to use the embedded camera with the demo application.
Please note that FaceCell source code EDK could be easily ported to most other platforms and processors.  

Licensing FaceCell EDK

To develop a product based on FaceCell technology, an integrator should obtain FaceCell 1.1 Library EDK (EUR 4,900) or FaceCell 1.1 source code EDK (EUR 17,190). The integrator can develop only an end-user product using FaceCell 1.1 EDK and sell/install the product to the end-users*. FaceCell 1.1 EDK customers can obtain additional FaceCell 1.1 licenses for their product installation or development at any time.

FaceCell 1.1 Library EDK
Customers should sign the FaceCell 1.1 Library EDK Software Licensing Agreement before purchasing FaceCell 1.1 Library EDK.
1,500 FaceCell 1.1 installation licenses are already included with the FaceCell 1.1 Library EDK license. Additional FaceCell 1.1 installation licenses may be purchased anytime.

FaceCell 1.1 source code EDK
Customers should sign the FaceCell 1.1 source code EDK Software Licensing Agreement before purchasing FaceCell 1.1 source code EDK.
10,000 FaceCell 1.1 installation licenses are already included with the FaceCell 1.1 source code EDK license. Additional FaceCell 1.1 installation licenses may be purchased anytime.
Please, contact us for more information about FaceCell 1.1 source code licensing.

* If the integrator wants to develop and sell a MegaMatcher, VeriFinger, VeriLook, FingerCell or FaceCell based development tool (with API, programming possibilities, programming samples, etc.), he/she will need a Neurotechnology permission and shall sign a special VAR agreement.

Download

• FaceCell 1.1 Algorithm Demo (for MS Windows Mobile 2003)
• FaceCell 1.1 EDK Trial

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