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!