1. Image Recognition Christian Cosgrove Kelly LiRebecca Lin Shree NadkarniSamanvit Vijapur Priscilla Wong Yanjun YangKate YuanDaniel Zheng Drew University New Jersey Governor’s School in the Sciences

3. Dog

5. What is image recognition?

6. Machine Learning Testing and Training

7. Database Feature Extraction Classification

8. Digit Database 515 images per digit

9. AT&T Face Database 40 subjects 10 images per subject

10. NJGSS Face Database 40 subjects 10 images per subject

11. Feature Extraction Vertical Edges Intensity Horizontal Edges Within each patch:

12. Classification Nearest NeighborCentroid

13. Classification Nearest NeighborCentroid

14. Output: Confusion Matrix

16. Network Graph

18. Digit Classification Performance WHAT IS “N PATCHES”? Number of patches 2 2 2 2 2 2 2 Nearest Neighbor Centroids 2 2 2 Number of patches Percent Accuracy

19. Facial Classification Performance Nearest Neighbor Centroids Number of patches 2 2 2 2 2 2 2 2 2 Percent Accuracy

20. Colored Nearest Neighbor Colored Centroids Grayscale Nearest Neighbor Grayscale Centroids Number of Training Pictures Percent Accuracy

21. Conclusions Optimal Performance Digits: 588 features, 14x14 patches 91% AT&T Grayscale Faces: 480 features, 16x8/8x4 patches 99% ± 0.67% NJGSS RGB Faces: 360 features, 16x8/8x4 patches 96% ± 1.20%

22. Conclusions Improvements Prioritization of feature More training Tolerance for rotation and reflections of subjects Tolerance for background differences/lighting Applications Personal security Social media Robotics Medical examination

23. Acknowledgements Dr. Minjoon Kouh Mr. Michael Clancy NJ Governor’s School in the Sciences Dr. Adam Cassano Dr. Steve Surace Yumi Kouh Bayer Health Care Dr. Robert Mayans AT&T Laura (NJGSS ’86) and John Overdeck NJGSS Alumnae and Parents of Alumnae Board of Overseers, New Jersey Governor’s Schools State of New Jersey Drew University And all of NJGSS’s generous sponsors!