Feature Extraction(2): Feature Extraction(2) Eigenface(Cont Eigenface(Cont Problen size, or scale, is misjudged. The head size in the input image must be close to that of the eigenfaces for the known, a sample system achieved approximately 96% correct classification averaged over lighting variation 85% correct averaged over orientation variation, and 64% correct averaged over size variation Eigenfaces Developer Feature Extraction(3) Visage Technology) Geometry-Feature-Based Method 28 archetypes on record OUsing geometric information of different parts of the face DIfferences/similarities with models like eyes, nose, mouth, chin, cheekbones etc, as features on record Use eigenface-based of the face, for instance, distance between eyes, width of O Map characteristics of a OPosition relationship between face parts, such as eyes face into a multi-dimensional face nose. n outh and chin, their shapes and sizes have OUse in conjunction with identification cards(e gvernment ID cards)in driver's licenses and pRoblem: geometry features can not be calculated sImilar go accurately, which effects the recognition capacity directly many States of Ohttp://www.visage.com/facialrecog.htm Geonet Feature Extraction (3): Feature Extraction(4: Geometry-Feature(Cont) Local Feature Analysis (LFA RElated to Eigenface, but more capable of accommodating changes in appearance or facial aspects UTilize features from different regions of the face, and incorporates the relative location of these features Scan the shapes search for facial features like nose, eyes 西百 choose dots(anchor point Connect dots to make a triangle net. Encode the result to a long number(key), 672 1s and Os7 Biometrics Research Centre (UGC/CRC) Lecture 8 - 37 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface (Cont.) (Cont.) Training Images Eigenfaces Biometrics Research Centre (UGC/CRC) Lecture 8 - 38 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface (Cont.) (Cont.) ‰Recognition performance decreases quickly as the head size, or scale, is misjudged. The head size in the input image must be close to that of the eigenfaces for the system to work well ‰In the case where every face image is classified as known, a sample system achieved approximately 96% correct classification averaged over lighting variation, 85% correct averaged over orientation variation, and 64% correct averaged over size variation Problems: Biometrics Research Centre (UGC/CRC) Lecture 8 - 39 Eigenfaces Eigenfaces Developer Developer (Viisage Technology) ‰128 archetypes on record ‰Differences/similarities with models on record Use eigenface-based recognition algorithm ‰Map characteristics of a person’s face into a multi-dimensional face space ‰Use in conjunction with identification cards (e.g. driver’s licenses and similar government ID cards) in many States of US ‰http://www.viisage.com/facialrecog.htm Biometrics Research Centre (UGC/CRC) Lecture 8 - 40 ‰Using geometric information of different parts of the face like eyes, nose, mouth, chin, cheekbones etc, as features of the face, for instance, distance between eyes, width of nose, etc. ‰Position relationship between face parts, such as eyes, nose, mouth and chin, their shapes and sizes have strong contribution to classify faces ‰Problem: geometry features can not be calculated accurately, which effects the recognition capacity directly Feature Extraction (3): Feature Extraction (3): Geometry Geometry-Feature Feature-Based Method Biometrics Research Centre (UGC/CRC) Lecture 8 - 41 Feature Extraction (3): Feature Extraction (3): Geometry Geometry-Feature (Cont.) Feature (Cont.) Biometrics Research Centre (UGC/CRC) Lecture 8 - 42 Feature Extraction (4): Feature Extraction (4): Local Feature Analysis (LFA) Local Feature Analysis (LFA) ‰Currently used by Visionic’s FaceIt software ‰Related to Eigenface, but more capable of accommodating changes in appearance or facial aspects ‰Utilize features from different regions of the face, and incorporates the relative location of these features ‰Represent facial images in terms of local statistically derived building blocks - Scan the shapes / search for facial features like nose, eyes. z Analyze pixels and facial protrude such as nose and cheekbones. z choose dots (anchor points). z Connect dots to make a triangle net. z Measure the angles of net. - Encode the result to a long number (key), 672 1’s and 0’s