Transcript Automated Facial Recognition:

Automated Facial Recognition:
Overview and Applications
by Brandon Hume
Human vs. Computer Vision
Computer vision still has a long way to
go to match the capabilities of human
face recognition.
People use non-face features to aid in
recognition.
Human recognition is biased, and has
memory limitations.
Applications
Information Security
Law enforcement
Social / Entertainment
Security and Surveillance
Recognition Steps
Detection and rough normalization of
faces
Feature extraction and accurate
normalization of faces
Identification and/or verification.
General Pattern Matching
Methods
Holistic - use the whole face region as the
raw input to a recognition system
Feature-based - use individual features to
verify matches
Hybrid - incorporate aspects of both holistic
and feature-based recognition.
Algorithms
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Iterative Closest Point (ICP)
Eigenfaces - Uses PCA analysis to derive a set
of "standardized face ingredients", from
statistical analysis of a database of face
images
Eigenface images
From AT&T
Laboratories
Cambridge
Complications
Lighting /
illumination
Distance
Rotation
Expression
Age
Blur
Obstructions
(hair, clothing, and
glasses)
Expressions
Lighting
Testing
Probability of Detection (Pd)
False Alarm Rate
Missed Alarm Rate
Image Databases
FERET - 14,126 total images
FRVT - Face Recognition Vendor Test
121,589 images
Ethics
Security vs. Privacy
Recent News – Google, Recognizr
Open Source
Colorado State University’s http://www.cs.colostate.edu/evalfacerec/
OpenCV http://code.google.com/p/opencvdotnet/
EmguCV http://sourceforge.net/projects/emgucv/files/
Questions??