Feature Recognition(8) Neural Networks Method Face Recognition Developer aTrain a neural network with samples (Cognitec's FaceVACS O Acquire implicit expression for the rules of face recognition O Technology used is believed to be neutral nets Derive face features by other methods and then design neural video camera (or even a r Deriving features and classifying faces are completed with neural standard webcam) cessing algorithm and 8予 Ire it with user's reference set stored in database ♀♀9 systems. delproducts-entry htm ARN Face Recognition Developer ZN-Face Extending face recognition a Use an extension of the Elastic Graph atching Algorithm DMost systems acquire faces under controlled lighting and a Can perform image acquisition, face calization and identification in 3.5 OHID project seeks to extend that to greater distances O Allows robust identification of persons OHow much can we improve face recognition by previously stored recognition from video vs recognition from still images a Reliable rejection of unknown persor Choosing keyfaces a Use in areas-air traffic, identity ages is documents, forensic investigation, ID Face images from a sequence may contain multiple views surveillance lighting expression Face Applicati Fa Applications range from static, mug-shot verification to a dynamic, uncontrolled face entification and tracking in a cluttered background Face Recognition Application et Referance Face Access Smart Card Allowed Access 99 Biometrics Research Centre (UGC/CRC) Lecture 8 - 49 Feature Recognition (8): Feature Recognition (8): Neural Networks Method Neural Networks Method ‰Train a neural network with samples ‰Acquire implicit expression for the rules of face recognition ‰Two types : ¾ Derive face features by other methods and then design neural network classifier ¾ Deriving features and classifying faces are completed with neural network Output Layer Hidden Layer Input Layer Biometrics Research Centre (UGC/CRC) Lecture 8 - 50 Face Recognition Face Recognition Developer Developer (Cognitec’s FaceVACS) ‰Technology used is believed to be neutral nets ‰Take user’s face image with a video camera (or even a standard webcam) ‰Extract feature using its image processing algorithm and compare it with user’s reference set stored in database ‰http://www.cognitec￾systems.de/products-entry.htm Biometrics Research Centre (UGC/CRC) Lecture 8 - 51 Face Recognition Face Recognition Developer Developer (ZN-Face) ‰ Use an extension of the Elastic Graph Matching Algorithm ‰ Can perform image acquisition, face localization and identification in 3.5 seconds ‰ Allows robust identification of persons previously stored ‰ Reliable rejection of unknown persons ‰ Use in areas - air traffic, identity documents, forensic investigation, ID systems, access control and video surveillance ‰ http://www.zn-ag.com/content.en/face.htm Biometrics Research Centre (UGC/CRC) Lecture 8 - 52 Extending face recognition Extending face recognition ‰Most systems acquire faces under controlled lighting and geometry ‰HID project seeks to extend that to greater distances ‰How much can we improve face recognition by recognition from video vs recognition from still images zChoosing ‘keyfaces’ zSequence of face images is not independent zFace images from a sequence may contain multiple views / lighting / expressions Biometrics Research Centre (UGC/CRC) Lecture 8 - 53 Face Recognition Application Biometrics Research Centre (UGC/CRC) Lecture 8 - 54 Face Applications • Applications range from static, mug-shot verification to a dynamic, uncontrolled face identification and tracking in a cluttered background Smart Card Access Control