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Neurotechnology VeriLook 3.2 Face Recognition Algorithm
Neurotechnology VeriLook 3.2 Face Recognition Algorithm
  • Supplier: Neurotechnology
  • Region: Lithuania
  • Updated: 07/20/2009
  • Click Frequency: 2115
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Product Specifications
  • Features:
    * Simultaneous multiple face processing. VeriLook 3.2 performs fast and accurate detection of multiple faces in live video streams and still images. All faces on the current frame are detected in 0.07 seconds and then each face is processed in 0.13 seconds
    * Live face detection. A conventional face identification system can be easily cheated by placing a photo of another person in front of a camera. VeriLook is able to prevent this kind of security breach by determining whether a face in a video stream belongs to a real human or is a photo.
    * Face image quality determination. A quality threshold can be used during face enrollment to ensure that only the best quality face template will be stored into database.
    * Multiple samples of the same face. Biometric template record can contain multiple face samples belonging to the same person. These samples can be enrolled with different face postures and expressions, from different sources and in different time thus allowing to improve matching quality. For example a person could be enrolled
    with and without eyeglasses or with different eyeglasses, with and without beard or moustache, with different face expressions like smiling and non-smiling etc.
    * Identification capability. VeriLook functions can be used in 1-to-1 matching (verification), as well as 1-tomany mode (identification).
    * Fast face matching. The VeriLook 3.2 face template matching algorithm compares 100,000 faces per second.
    * Compact face features template. A face features template occupies only 2.3
    * Features generalization mode. This 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.