Face Recognition Systems
TEAM-1
JACKIE ABBAZIO
SASHA PEREZ
DENISE SILVA
ROBERT TESORIERO
Overview: Face Biometrics
Facial recognition through the use of computer
analysis of facial structure.
Software measures a number of points of facial
characteristics such as eyes, nose, mouth, angles of
key features, and lengths of various portions.
Collected data is used to create a template through
a mathematical algorithm (Neural-Networks,
Eigenface) and the file is stored within the
database.
File is compared to other files within the database
in search of an identity match.
Face Biometrics Continued
Face biometric systems employee the capturing of
facial pattern characteristics through the use of still
photography or video clips.
Pattern recognition software relies on:
Data collection raw data
Feature extraction eyes, nose, mouth, etc
Classification class the object is placed into (male or female,
skin tone, etc)
FAR & FRR
FAR ( False Acceptance Rating) the false
acceptance rating is the probability that the software
will incorrectly declare a successful match between
the input data against the database.
FRR (False Rejection Rating) the false rejection
rating is the probability that the software will declare
a failure to match the input data against the
database.
Face Biometric Systems Project
The face biometric
systems project involved
the research and testing
of various facial
biometric softwares
based on criteria set by
the clients.
Two face biometric
softwares were chosen
and tested (Luxand
FaceSDK & VeriLook).
Software Comparison Table
Luxands FaceSDK 1.7
After extensive testing and researching the Face
Biometric Systems, we recommend purchasing
Luxands FaceSDK 1.7 software. It has the following
strengths:
Easy to use
Ability to enroll all images
Matches work best at FAR of 50%, but produces matches at
FAR of 10%.
Works best for aging
*Free demo version used
Luxand FaceSDK Example
Enrolled into class
database
This 1979 image matched with 2007
image
2008 image with 51% similarity
Luxand FaceSDK Similarity Matrix
VeriLook 3.2
Designed for biometric system developers and
integrators.
Allows for easy integration and rapid development of
biometric applications using functionality.
Can perform simultaneous multiple face detections
with the ability to process 100,000 faces per second
and it recommends the minimum image size to be
640x480 pixels .
Software works best
with high resolution
photos.
False Acceptance
Rating set for 100%
All images matched
100% against the same
image in the database.
The score of 180 is
interpreted as an exact
match.
Free demo version
used
VeriLook Test Result
The results show that the
photo from 1969
matched a photo from
2008 with a similarity
score of 18 or 10%.
This result is comparable
with the FaceSDK age
identification test, where
the same image from
1969 matched the same
photo from 2008 with a
61.9% similarity rate.
VeriLook Aging Result
Software Comparison Test
Luxand FaceSDK - VeriLook
Tests were run using
both Luxand FaceSDK
1.7 and VeriLook using
the four photos seen
here.
Similarity ratings varied
from one software to
the other.
Luxand FaceSDK
results provided more
results based on
Similarity Rating than
VeriLook .
Luxand VeriLook Comparison
VeriLook Identification and
Authentication Results
FaceSDK Identification and
Authentication Results
Conclusion
Luxand FaceSDK 1.7
works very well in identifying face similarity among people in a group
worked relatively well matching an image of the subject as a child
VeriLook 3.2
had more limitations than FaceSDK, it only accepted high-res images.
results for the similarity test were lower than the FaceSDK software
PDA Security
none of the software tested was suitable for PDA security use
Further Work
we recommend further work using 3-D face biometrics software and
scanners to find optimal solution for PDA security
