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1,1,Some Recent Development of Intelligent PR and Applications,Guanghui He ,2,2,What are Biometrics? Biometrics are automated methods of recognizing a person based on the acquired physiological or behavioral characteristics,Percentage of usage (Source: International biometric group),3,3,A Scenario Two Al Qaeda（“基地”组织） suspects were recently taken into custody by U.S. immigration authorities as they tried to enter the United States after their fingerprints were matched with ones lifted by U.S. military officials from documents found in caves in Afghanistan（阿富汗）.,Why Biometric Technologies? For Security Reasons,4,4,Example 1: SFinGe - Synthetic Fingerprint Generator developed at the Biometric Systems Lab,University of Bologna ITALY, is utilized to:,compare different fingerprint matching algorithms,train pattern recognition techniques that require large learning-sets (e.g. neural network),easily generate a large number of “virtual users” to develop and test medium/large-scale fingerprint-based systems,5,5,3-D model (pressure in on-line model),Modeling by deformation,Modeling segments (conics, splines),Example 2: generation of synthetic signature,Assembling (desegmentation) of 2-D model,6,6,Example 3: Privacy protection: After enrollment, a true object (e.g. image of face, fingerprint or voice signal) is intentionally distorted using irreversible transform - Cancelable biometrics (Ratha, Connell, Bolle, 2001),Skin distortion (fingerprint) (source: Biometric Systems Lab, University of Bologna),Face image is warped with bilinear interpolation (source: Serif Inc.),Some More Examples: Generation of synthesis fingerprints Generation of synthetic signatures (handwriting modeling is a relevant problem) Iris recognition and synthesis Information fusion in biometrics Speech-to-animated-face,7,8,Where do we need biometrics?,Traditional application: human identification Recent advances: Early warning paradigm Designing simulators for HQP training systems Sensing in robotics,9,Early detection and warning,Semantic domain,Biometric sensor,Signal processing,Decision making,Raw biometric data,Basic configuration,Feature space,Application: physical access control system,Sensors,Extractors Image- and signal processing algorithm,Classifiers,Biometrics Voice, signature, face, fingerprint, iris, hand geometry, etc,Data Rep. Audio signal, image, infrared image,Feature Vectors,Scores,Decision: Match, Non-match, Inconclusive,Biometric databases,Level 1: document-check,Databases (Watch-list),Level 2: biometrics,10,11,Laboratory experiments,12,Early warning system components:,- Supports facial analysis Skin temperature evaluation Detection of disguise: wig and other artificial materials, and surgical alternations Evaluation of blood vessel flow (modeling expressions) Other physiological / medical measurements (alcohol / drug abuse),Infrared biometrics and decision support,Mid-infrared: 3-5 m, far-infrared: 8-12m,Temperature value 32.8754 0C is detected in a point,13,Early warning system components:,Blood flow rate analysis (from infrared),Visualization of the blood flow rate from the upper rectangle of (a),Thermal image of subject at the beginning of answering the question “Do you have that stolen $20 on you right now?”,Thermal image of subject at the end of answering the question,Visualization of the blood flow rate from (b). The difference is significant (from I. Pavlidis report),14,Early warning system: decision-making,insufficiency of information,INDIVIDUAL biometrics,Degrees of belief,Biometric sensor,TEMPORAL faults of biometric sensors,errors of biometric sensors,Mass assignments,Belief function,Updating,Decision making in semantic form,15,Early warning security access control system:,Semantic processor,Gait-biometric processor,Gait features processor,The ground reaction force,Gender Pregnancy Fatigue Injuries Afflictions Drunkenness,Ground reaction force processor,Discriminative gait biometric in semantic form,Gait biometrics analysis and decision-making assistance,16,Face capturing,Fitting points,0001001001010011010010010010010110010010001000010010110100100101001001001000 ,File (mesh/colour),3D Face model,Early warning system components:,17,Face capturing,Fitting points,0001001001010011010010010010010110010010001000010010110100100101001001001000 ,File (mesh/colour),3D Face model,Early warning system components:,18,Other applications: Biometric data modeling for HQP training,Processing of screened data,Processing of pre-screened data,Dialog support,Decision-making support,Visible band camera,IR band camera,Synthetic image of an individual,Voice analyzer,Officer-in-training,19,Perspectives: humanoid robots,Sensing in robotics,Robot head developed by Dr. Marek Perkowski at Portland State University,Emotion synthesis,Robot speech,20,20,Its Similarity and Pattern Matching!,What is Measurement ?,Just a Comics Joke?,No! More Than That,21,21,Pattern Recognition,Cognition (Learning) Re-Cognition Classification Identification Verification Clustering,22,22,3D Object Recognition,23,23,Table of Contents,BACKGROUND THEORY EXPERIMENTS and ILLUSTRATIONS FUTURE RESEARCH,24,24,Linear Combination,Object 1 A1 Object 2 A2 Object 3 A3 Object 4 A4 Object A4= a A1+ bA2 +cA3 +d,25,25,3D Recognition Background,Widely used industrial parts inspection military target identification CAM/CAD engineering design image/vision understanding, interpretation, visualization, and recognition,26,26,3D Recognition Background,Recognition 3D objects Rigid Objects Fixed shapes Deformable Objects Variable shapes Articulated Objects Fewer methods proposed Brooks ACRONYM system using symbolic reasoning. Grimson et al extended the interpretation of tree approach to deal with 2-D objects with articulated components,27,27,3D Recognition Background,Extended Linear Combination Method (LC) Simpler preprocessing Simpler and faster computation Applicable to many articulated object recognition, understanding, interpretation, and visualization,28,28,THEORY,Extended Linear Combination Method (LC) based on the observation that novel views of objects can be expressed as linear combination of the stored views (from learning) It identifies objects by constructing custom-tailored templates from stored two-dimensional image models.,29,29,Linear Combination,Model an image consists of a list of feature points observed in the image,30,30,Linear Combination,Recognition: An unknown object is matched with a model by comparing the points in an image of the unknown object with a template-like collection of points produced from the model,31,31,32,32,33,33,34,34,35,35,Experitment-1Match same objects,36,36,Experiment-1 Result,37,37,Experiment-2,38,38,Experiment-3 ,39,39,Experiment-3 Result,40,40,Experiment-4,41,41,Experiment-4 Result,Rejected,Rejected Too,42,42,43,43,44,44,45,45,46,46,Color Biometric Imaging Analysis,47,47,Items to be discussed:,Clustering and K-means algorithm Statistical Unsupervised Color Representation and Color Image Segmentation,48,48,Supervised Classification and minimum distance classification,Minimum Distance Classification Supervised Find the center of known patterns of each class Classify unknown patterns into the class that is “closest” to it.,49,49,Color Image Segmentation: Hue Component,50,50,Color Image Segmentation,Task: Study the K-means algorithm in hue space. Interesting: Periodical Circular Property of hue component new Measure of Distance. Problem: K-means algorithm is based on the measure of distance and definition of center,51,51,Hue Component Clustering,Definition 1: Distance of Hue Values Definition 2: Directed Distance of Hue Values Tricky: Addition of Directed Distance Definition 3: Interval and Its Midpoint in H Space. Definition 4: Center of a Set of Points in Hue Space Theory: Euclidean Theory of Center in Hue Space,52,52,Hue Component Clustering,Definition 1: Distance of Hue Values,53,53,Hue Component Clustering,Definition 2: Directed Distance of Hue Values Tricky: Addition of Directed Distance the following vector addition property no longer holds:,54,54,Hue Component Clustering,Revisit definition: Interval and Its Midpoint in H Space. Revisit definition : Center of a Set of Points in Hue Space Revisit the Proof of Theory: Euclidean Theory of Center in Hue Space,55,55,Color Image Segmentation,I and H components are of Interest. Good color image segmentation algorithms should consider and combine both Variation of light intensity and occlusion: hue component is better Color information is lost: Intensity component is better Fuzzy member function is introduced,56,56,Color Image Segmentation - Experiment 1Intensity Distinguishable,(a) Original color image,57,57,Color Image Segmentation - Experiment 1Intensity Distinguishable,(b) Intensity image,58,58,Color Image Segmentation - Experiment 1Intensity Distinguishable,(c) Hue image,59,59,Color Image Segmentation Experiment Intensity Distinguishable,(d) Segmentation by hue,60,60,Color Image Segmentation - Experiment 1Intensity Distinguishable,(e) Segmentation by hue and intensity,61,61,Color Image Segmentation - Experiment 2 Hue Distinguishable,(a) Original color image,62,62,Color Image Segmentation - Experiment 2 Hue Distinguishable,(b) Intensity image,63,63,Color Image Segmentation - Experiment 2 Hue Distinguishable,(c) Hue image,64,64,Color Image Segmentation - Experiment 2 Hue Distinguishable,(d) Segmentation by intensity,65,65,Color Image Segmentation - Experiment 2 Hue Distinguishable,(e) Segmentation by hue and intensity,66,66,Some More Illustrative Examplesof Medical Imaging Results,67,67,68,68,69,69,70,70,71,71,72,72,73,73,74,74,75,75,76,76,77,77,PR (Pattern Recognition) andAI (Artificial Intelligence),
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