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Image Recognition
Christian Cosgrove
Kelly Li Rebecca Lin
Shree Nadkarni Samanvit Vijapur
Priscilla Wong
Yanjun Yang Kate Yuan Daniel Zheng
Drew UniversityNew Jersey Governor’s School in the
Sciences
Dog
What is image recognition?
Machine Learning
• Testing and Training
Database
Feature Extraction
Classification
Digit Database
• 515 images per digit
AT&T Face Database
• 40 subjects
• 10 images per subject
NJGSS Face Database
• 40 subjects
• 10 images per subject
Feature Extraction
Vertical Edges
Intensity
Horizontal Edges
Within each patch:
Classification
Nearest Neighbor Centroid
Classification
Nearest Neighbor Centroid
Output: Confusion Matrix
Output: Confusion Matrix
Network Graph
Network Graph
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
Per
cen
t Acc
urac
y
Facial Classification Performance
Nearest Neighbor
Centroids
Number of patches
2 2 2 2 2 2 2 2 2
Per
cen
t Acc
ura
cy
Colored Nearest Neighbor
Colored Centroids
Grayscale Nearest Neighbor
Grayscale Centroids
Number of Training Pictures
Per
cen
t Acc
ura
cy
Conclusions
Optimal Performance• Digits: 588 features, 14x14 patches
91%• AT&T Grayscale Faces: 480 features, 16x8/8x4
patches99% ± 0.67%
• NJGSS RGB Faces: 360 features, 16x8/8x4 patches96% ± 1.20%
ConclusionsImprovements• 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
Acknowledgements
Dr. Minjoon KouhMr. Michael Clancy
NJ Governor’s School in the Sciences
Dr. Adam Cassano Dr. Steve Surace
Yumi KouhBayer 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
SchoolsState of New Jersey
Drew University
And all of NJGSS’s generous sponsors!