LFA Developer LFA Developer (Visionic's Facelt Visionic's Facelt Software 口 Represent facial in terms of local statistically ONodal points are measured to generate a number, call a faceprint, 84 bytes in size JFaceprint can be matched or compared with others OFaceprint is resistant to changes in lighting, facial e Distance between eyes expression and is robust with respect to pose variations, up o Width to 35 degrees a Being incorporated into a Close Circuit Television anti- ● Cheekbone crime system in UK ●Chn at 26 June 2002 aUse local feature analysis(geometric-feature based Jhttp://www.identix.com/products/prosecuritybnpargushtml Feature Extraction(5): Feature Extraction(6: Local Representation- Gabor Jets Handling Lighting and Pose Sur ace a Control the lighting or pose ation of small face regions using Gabor filter a Capture lighting variability scales x 8 orientations a Feature points on a regular lattice, or chosen to be salient points 口 Create s3 D model 口 Feature Use Pose correction O Aggregate score gives total similarity] reprocessing step for a variety of algorithms to substantially improve the ability to recognize non-frontal feature comparis faces O Model the lighting -Belhumeur Deformation Models Color Feature Extraction(7): OThis model considered distortion characteristics of faces Deformation Models(Cont e.g. the face image may vary in terms of sizes, angles, and vary when the person smile REcognize distortion invariant object by expressing them a sparse graph whose vertices can be marked with multi-resolution description of local energy spectrum, and whose edges show topological relation between vertices JA face in normal condition can be expressed by a Models for face parts in deformation template method dFace recognition is transformed as a graphic matching 88 Biometrics Research Centre (UGC/CRC) Lecture 8 - 43 LFA Developer Developer (Visionic’s FaceIt) ‰Represent facial images in terms of local statistically derived building blocks ‰Identify 80 nodal points on a face z Distance between eyes zWidth of nose z Depth of eye sockets z Cheekbones z Jaw line z Chin ‰Use local feature analysis (geometric-feature based method) Biometrics Research Centre (UGC/CRC) Lecture 8 - 44 LFA Developer Developer Visionic Visionic’s FaceIt Software Software ‰Nodal points are measured to generate a number, call a faceprint, 84 bytes in size ‰Faceprint can be matched or compared with others ‰Faceprint is resistant to changes in lighting, facial expression and is robust with respect to pose variations, up to 35 degrees ‰Being incorporated into a Close Circuit Television anti￾crime system in UK ‰Visionics Corporation has merged with Identix Incorporated at 26 June, 2002 ‰http://www.identix.com/products/pro_security_bnp_argus.html Biometrics Research Centre (UGC/CRC) Lecture 8 - 45 Feature Extraction (5): Feature Extraction (5): Local Representation- Gabor Jets ‰ Used in Elastic Bunch Graph methods z Von der Malsburg et al. ‰ Representation of small face regions using Gabor filter responses z ~4 scales x 8 orientations ‰ Feature points on a regular lattice, or chosen to be salient points z Features are “self-localizing” ‰ Feature points compared pairwise z Aggregate score gives total similarity] ‰ Elastic bunch graph involves displacement between features as well as feature comparison Biometrics Research Centre (UGC/CRC) Lecture 8 - 46 Feature Extraction (6): Feature Extraction (6): Handling Handling Lighting and ighting and Pose ‰ Control the lighting or pose ‰ Simple normalization (e.g. mean subtraction) ‰ Capture lighting variability ‰ Enroll multiple views ‰ Create s 3D model z Use Pose correction z e.g. FRVT uses Blanz & Vetter’s 3D morphable models as a preprocessing step for a variety of algorithms to “…substantially improve the ability to recognize non-frontal faces.” ‰ Model the lighting - Belhumeur Biometrics Research Centre (UGC/CRC) Lecture 8 - 47 Feature Extraction (7): Feature Extraction (7): Deformation Models Deformation Models ‰This model considered distortion characteristics of faces, e.g. the face image may vary in terms of sizes, angles, and vary when the person smile ‰Recognize distortion invariant object by expressing them in a sparse graph whose vertices can be marked with multi-resolution description of local energy spectrum, and whose edges show topological relation between vertices, and edges have distance property ‰A face in normal condition can be expressed by a uniform image ‰Face recognition is transformed as a graphic matching problem Biometrics Research Centre (UGC/CRC) Lecture 8 - 48 Feature Extraction (7): Feature Extraction (7): Deformation Models (Cont.) Deformation Models (Cont.) Models for face parts in deformation template method