FaceCell, Embedded Face Recognition Technology

Content of this page

• Why FaceCell?
• Algorithm
• Specifications
• Algorithm's demo
• Related products

Why FaceCell?

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

• 
  Click to zoom
  Reliability. The FaceCell technology is intended for hardware with lower computational capabilities than PCs. Compared to the PC-based VeriLook 3.1 algorithm, the FaceCell 1.1 algorithm has a higher, but acceptable False Rejection Rate. The graphical chart compares FaceCell 1.1 ROC with VeriLook 3.1 ROC using face images from XM2VTSDB database.
• Identification ability. FaceCell is designed not only for verification (1:1 matching), but also for identification (1:N matching). The algorithm is able to match up to 3,000 faces per second.
• Easy integration. FaceCell can be used in a wide range of applications and can be easily integrated into handheld or embedded devices with built-in video cameras, such as PDAs and smart phones, without having to develop any special hardware.
• Portability. FaceCell Embedded Development Kit is designed for easy implementation into very various and specific applications. The algorithm's ANSI C source code can be ported to various platforms and hardware.
• Embedded and PC-based multi-biometric capable technologies from the same vendor. Combined with our other technologies, FaceCell could be used in developing these advanced systems:
  • Multi-biometric embedded systems, using FaceCell EDK together with FingerCell EDK.
  • Mixed embedded/PC systems, using FaceCell EDK together with VeriLook Standard SDK.
  • Complex multi-biometric embedded/PC systems, using a combination of FaceCell EDK, FingerCell EDK, VeriLook SDK and VeriFinger SDK.

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

Algorithm's demo

The FaceCell 1.1 demo application for Microsoft Windows CE can be downloaded for evaluation of the FaceCell 1.1 face recognition algorithm. The application enrolls and identifies faces from image files, embedded cameras and external video sources. The device must be running MS Windows Mobile 5 to use the embedded camera with the demo application. Internet connection is not required to run the application.

FaceCell 1.1 EDK trial is also available for downloading.

Related products

• FaceCell 1.1 Library EDK
• FaceCell 1.1 source code EDK