Project I. Face AnalysisProject II: Face DetectionSlide 3Examples of PatternsSlide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13ApplicationsTwo Schools of ThinkingLevels of taskSchools and streamsAn example of Pattern RecognitionFeatures and DistributionsDecision/classification BoundariesMain Issues in Pattern RecognitionWhat is a pattern?What is a patternSlide 24eigen-facesProject I. Face AnalysisProject II: Face DetectionLecture 1:Introduction to Pattern Recognition 1. Examples of patterns in nature. 2. Issues in pattern recognition and an example of pattern recognition 3. Schools in pattern recognition 4. Pattern theoryExamples of PatternsCrystal patterns at atomic and molecular levelsTheir structures are represented by 3D graphs and can be described by deterministic grammar or formal languageExamples of PatternsConstellation patterns in the sky.The constellation patterns are represented by 2D (often planar) graphsHuman perception has strong tendency to find patterns from anything. We see patterns from even random noise --- we are more likely to believe a hidden pattern than denying it when the risk (reward) for missing (discovering) a pattern is often high.Examples of PatternsBiology pattern ---morphologyLandmarks are identified from biologic forms and these patterns are then represented by a list of points. But for other forms, like the root of plants,Points cannot be registered crossing instances.Applications: biometrics, computational anatomy, brain mapping, …Examples of PatternsPattern discovery and associationStatistics show connections between the shape of one’s face (adults) and his/her Character. There is also evidence that the outline of children’s face is related to alcohol abuse during pregnancy.Examples of PatternsWe may understand patterns of brain activity and find relationships between brain activities, cognition, and behaviorsPatterns of brain activities:Examples of PatternsPatterns with variations: 1. Expression –geometric deformation 2. lighting --- photometric deformation 3. 3D pose transform 4. Noise and occlusionExamples of PatternsA wide variety of texture patterns are generated by various stochastic processes. How are these patterns represented in human brain?Examples of PatternsSpeech signal and Hidden Markov modelExamples of PatternsNatural language and stochastic grammar.Examples of PatternsApplicationsLie detector,Handwritten digit/letter recognitionBiometrics: voice, iris, finger print, face, and gait recognitionSpeech recognitionSmell recognition (e-nose, sensor networks)Defect detection in chip manufacturingReading DNA sequencesFruit/vegetable recognitionMedical diagnosisNetwork traffic modeling, intrusion detection… …Two Schools of Thinking1. Generative methods: Bayesian school, pattern theory. 1). Define patterns and regularities (graph spaces), 2). Specify likelihood model for how signals are generated from hidden structures 3). Learning probability models from ensembles of signals 4). Inferences.2. Discriminative methods: The goal is to tell apart a number of patterns, say 100 people in a company, 10 digits for zip-code reading. These methods hit the discriminative target directly, without having to understand the patterns (their structures) or to develop a full mathematical description. For example, we may tell someone is speaking English or Chinese in the hallway without understanding the words he is speaking. “You should not solve a problem to an extent more than what you need”Levels of taskFor example, there are many levels of tasks related to human face patterns 1. Face authentication (hypothesis test for one class) 2. Face detection (yes/no for many instances). 3. Face recognition (classification) 4. Expression recognition (smile, disgust, surprise, angry) identifiability problem. 5. Gender and age recognition-------------------------------------------------------------- 6. Face sketch and from images to cartoon --- needs generative models. 7. Face caricature … … The simple tasks 1-4 may be solved effectively using discriminative methods, but the difficult tasks 5-7 will need generative methods.Schools and streamsSchools for pattern recognition can be divided in three axes: Axis I: generative vs discriminative (Bayesian vs non-Bayesian) (--- modeling the patterns or just want to tell them apart) Axis II: deterministic vs stochastic (logic vs statistics) (have rigid regularity and hard thresholds or have soft constraints on regularity and soft thresholding) Axis III: representation---algorithm---implementationExamples: Bayesian decision theory, neural networks, syntactical pattern recognition (AI), decision trees, Support vector machines, boosting techniques,An example of Pattern Recognition Classification of fish into two classes: salmon and Sea Bass by discriminative methodFeatures and DistributionsDecision/classification BoundariesMain Issues in Pattern Recognition1. Feature selection and extraction --- What are good discriminative features?2. Modeling and learning 3. Dimension reduction, model complexity4. Decisions and risks5. Error analysis and validation.6. Performance bounds and capacity.7. AlgorithmsWhat is a pattern?In plain language, a pattern is a set of instances which share some regularities,and are similar to each other in the set. A pattern should occur repeatedly.A pattern is observable, sometimes partially, by some sensors with noise anddistortions. How do we define “regularity”? How do we define “similarity”?How do we define “likelihood” for the repetition of a pattern?How do we model the sensors?What is a patternIn a mathematical language, Grenander proposed to define patterns with thefollowing components (1976-1995)1. Regularity R=<G, S, , > G --- a set/space of generators (the basic elements in a pattern), each generator has a number of “bonds” that can be connected to neighbors. S --- a transformation group (such as similarity transform) for the generators  --- a set of local regularities (rules for the compatibility of generators and their bounds  --- a set of global configurations (graphs with generators being vertices and connected bonds being edges).What is a pattern 2. An image algebra I =<C( R ), E> The regularity R defines a class of regular configurations C(R). But such configurations are hidden in signals, when a