[PPT] - Face Recognition on the MORPH-II Database Morgan Ferguson PowerPoint Presentation

SLIDE 1

Face Recognition on the MORPH-II Database

Morgan Ferguson

University of North Carolina at Wilmington

July 25, 2017

1 / 26

SLIDE 2

Face Recognition on the MORPH-II Database

Overview

Introduction/ Background Morph-II Database Face Recognition Our Work with Face Recognition Goals Methods Results Analysis Conclusion Future Research References

2 / 26

SLIDE 3

Face Recognition on the MORPH-II Database Introduction/ Background Morph-II Database

Morph-II Database

◮ Contains 55,134 mugshots of 13,617 individuals ◮ Collected over 5 years ◮ Ages range from 16 to 77 years ◮ Average of around 4 pictures per individual ◮ Provides race, gender, date of birth, date of arrest, age, age

difference since last picture, subject identifier, and picture number for each picture in the database

3 / 26

SLIDE 4

Face Recognition on the MORPH-II Database Introduction/ Background Morph-II Database

Challenges and Improvements

◮ Original data yields poor accuracy rates ◮ Images have different sizes and faces are in different locations ◮ Pre-processed images! ◮ Standardized data

Original Modified

4 / 26

SLIDE 5

Face Recognition on the MORPH-II Database Introduction/ Background Face Recognition

What is face recognition?

◮ Process of identifying a new face as a known individual or

unknown individual

◮ Face recognition works by training some classifier on a set of

images (training or gallery) and then matching new image (test or validation)

Figure: http://www.nec.com/en/global/solutions/safety/face_recognition/index.html 5 / 26

SLIDE 6

Face Recognition on the MORPH-II Database Introduction/ Background Face Recognition

Eigenfaces

◮ Face images are projected onto a feature space (”face space”) ◮ Face space is defined by ”eigenfaces”, or the eigenvectors of

the set of faces

◮ Ability to learn to recognize new faces in unsupervised manner

Figure: M. Turk, A. Pentland, ”Eigenfaces for Recognition”, J. Cognitive Neuroscience, vol. 3, no. 1, 1991. 6 / 26

SLIDE 7

Face Recognition on the MORPH-II Database Introduction/ Background Face Recognition

Fisherfaces

◮ Linear Discriminant Analysis (LDA) ◮ Maximizes distance between classes ◮ Supervised technique

Figure: http://www.scholarpedia.org/article/Fisherfaces 7 / 26

SLIDE 8

Face Recognition on the MORPH-II Database Introduction/ Background Face Recognition

Local Binary Patterns (LBP) Review

◮ Different approach to obtaining vectors from an image ◮ Labels pixels of an image by analyzing the neighbors of each

pixel and considers the result as a binary number

◮ LBP’s have parameters such as block size and radius

Figure: http://www.scholarpedia.org/article/Local_Binary_Patterns 8 / 26

SLIDE 9

Face Recognition on the MORPH-II Database Introduction/ Background Face Recognition

Classification and Distance Metrics

Support Vector Machine (SVM)

◮ Radial basis

Nearest Neighbor approach

◮ Euclidean distance ◮ Cosine distance ◮ Cityblock distance ◮ Bray-Curtis distance ◮ Canberra distance ◮ Mahalanobis Cosine distance

Figure: http://www.researchgate.net/figure/5423571_fig6_ Figure-13-313-Illustration-of-distance-measures

9 / 26

SLIDE 10

Face Recognition on the MORPH-II Database Our Work with Face Recognition Goals

Goals

Reproduce Results

◮ Compare benchmark results with previous research

New Trials

◮ Test out new subsets, distance metrics, feature vectors, etc.

Optimize

◮ Try out different methods to improve on initial results

Analyze

◮ Start creating tables and graphs to compare results ◮ Begin to make conclusions

10 / 26

SLIDE 11

Face Recognition on the MORPH-II Database Our Work with Face Recognition Methods

Experimental Setup

1. Subset Morph-II
2. Begin with input data (pre-processed images from subsets or

LBP feature vectors)

3. Break into training and testing data
4. Perform PCA (Eigenfaces) or LDA (Fisherfaces)
5. Use SVM or Nearest Neighbor
6. Classify test image as a subject from training images

11 / 26

SLIDE 12

Face Recognition on the MORPH-II Database Our Work with Face Recognition Methods

Subset Scheme 1

◮ Take subjects with more than 10 images each ◮ Randomly select 10 images for each person ◮ Match the number of males with the number of females (83

subjects each)

◮ Randomly select 1 image for each subject as testing image

and designate other 9 for training

◮ 166 subjects and 1660 total images

12 / 26

SLIDE 13

Face Recognition on the MORPH-II Database Our Work with Face Recognition Methods

Subset Scheme 2

◮ Take subjects with more than 10 images each ◮ Randomly select 10 images for each person ◮ Randomly select 5 images for each subject as testing image

and designate other 5 for training

◮ 544 subjects and 5440 total images

13 / 26

SLIDE 14