1. Adaptable Pattern-Classifier System Applying to Face Recognition Intelligent Technology Lab. B. Battulga

2. Research Field Research Field Computer Vision Computer Vision Face Detection and Recognition Face Detection and Recognition Pattern Classifier Pattern Classifier Our Classifier Fusion Methods and Results Our Classifier Fusion Methods and Results Further Research Direction Further Research Direction Contents

3. Evolvable & Adaptive System Machine Learning Intelligent Computer Vision Object Recognition & Detection Intelligent Home Network Computer Vision System Ubiquitous Computing Application Intelligent Technology Research Field

4. Determination of Local Image Properties Low-Level Vision Determinant of Genetic Scene Attributes Description of the Scene Intermediate VisionHigh-Level Vision Smoothing Edge Color Texture Pattern Recognition Model Matching Boundaries Regions & surfaces Understanding Semantics Context Future-Past Computer Vision

5. Image Recognition Method of Computer Vision

6. Preprocessing

7. Face Detection and Recognition Face Detection Face Recognition

8. Pattern Classifier

9. Face Detection as Experiment in Classifying System Individual Classifiers Techniques for Face Recognition - Eigenface - Fisherface - NN - Gabor - Wavelate… Individual Classifiers Techniques for Face Recognition - Eigenface - Fisherface - NN - Gabor - Wavelate… Classifying System Classifying System Preprocessing Classifier Classifier Fusion & Selection Result

10. Training Set and Classifiers Classifier compares test Data with Trained Data set. Classifier compares test Data with Trained Data set. - Not enough Training Set. - Various type of Poses and Lightning. - Various type of Environment

11. Our Classifier Fusion Methods and Results Our Create Fitness Correlation Classifier fusion method that brings the better reliance on robust training data set. Our Create Fitness Correlation Classifier fusion method that brings the better reliance on robust training data set. YaleDBFeretInhaDBalldel allhuge Gabor2890.9%85.4%89.4%98.5%95.1% PCA81.4%74.1% 87.9% PCA[6]55.2%65.1% 67.4% FitCorr[6] with G28 90.6%94.4%91.8%97.7%96.5% FitCorr[6] with PCA 65.7%71.9% 69.2% Oracle[6] with G28 99.0%96.6%94.3%99.4%98.6% Oracle[6] with PCA 89.5%93.2%

12. Absence of Features in Training Set

13. Pre Trained Condition Detection System for constructing Classifier System. - Anticipation of feature in particular condition Adaptable Classifier Combination Systems Further Research Direction

14. Other Laboratory Environment of Pattern Classifier Biometric Access: - Brain Wave - Genetic - Other Sensors Biometric Access: - Brain Wave - Genetic - Other Sensors Data Mining - Disease Detection by Medical Examination - Pattern Recognition in Financial and Economical side Data Mining - Disease Detection by Medical Examination - Pattern Recognition in Financial and Economical side Delta0.1-3 Hz Theta3-8 Hz Alpha8-12 Hz Low Beta12-15 Hz Midrange Beta15-18 Hz High Betaabove 18 Hz Gamma40 Hz

15. The End