(Spring 2013) Best Practices for Facial Recognition using Mobile Devices

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International Center for Biometric Research

The purpose of this study was to determine the best practices and characteristics for capturing a quality image with a mobile device that meets standards for facial recognition software. The focus of our study on image quality revolved around lighting, subject pose, and camera properties. Our analysis of image quality was based off of ISO standards for facial recognition.

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BEST PRACTICES FOR FACIAL RECOGNITION USING MOBILE DEVICES
The purpose of this study was to determine the best practices and characteristics for capturing a quality image with a mobile device that meets standards for
facial recognition software. The focus of our study on image quality revolved around lighting, subject pose, and camera properties. Our analysis of image quality
was based off of ISO standards for facial recognition.
Chris Nagelbach, James Sternberg, Cody Maus, William Payne, Michael Brockly, Stephen Elliot
Overview
Distance Angle Type
Lighting
1.5 m, depth of field 10
cm
45 degrees left/right
center, 35 degrees
above center
Dual Lighting, 800-900
Lux
Pose/Person
Eye width should be
25% of picture width
and located at 60% of
total image height
+/- 5 degree from
center
Neutral emotion with
naturally open eyes, 0
motion blur
Camera Between 0.5 to 1 m
+/- 5 degrees
above/below eye level
Back facing camera
Background N/A N/A Illuminated 18% grey
Best Practices
Camera Distance From
Face
Eye Separation Compliant Failure Rate
0.5M 436.53 OK 0%
1.0M 182.48 OK 0%
1.5M 129.7 OK 0%
2.0M 104.52 Fail 100%
Lighting Type
Percent Facial
Brightness
Compliant
Failure
Rate
Overhead Fluorescent
Lighting
50.65 OK 7%
2 Studio Lamps at Eye Level
45˚ From Center
60.86 OK 0%
2 Studio Lamps 35˚ Above Eye
level 45˚ from center
61.13 OK 0%
Percent Background
Gray
Compliant
Failure
Rate
Percent Facial
Brightness
Compliant
Failure
Rate
Eye
Separation
Compliant
Failure
Rate
Rear facing camera with white
background
12.65673772 OK 0% 47.3 OK 0% 477.9623327 OK 0%
Front facing camera with white
background
8.808231499 OK 0% 34.7 Fail 30% 122.5834791 Fail 10%
Rear facing camera with 18% grey
background
42.66904296 OK 0% 62.9 OK 0% 607.0340979 OK 0%
Front facing camera with 18% grey
background
39.84486436 OK 0% 57.6 Ok 0% 121.4669226 Fail 20%
Conclusions
Camera Distance From Face Lighting
Camera and Background
Ideal Experiment Setup
Based on our research and experimentations we have determined the ideal
best practices listed in the best practices table above. We came to this
conclusion by taking 10 pictures for each of the desired variables on an iPhone
4s. The pictures were processed against the ISO_FRONTAL_Best_Practices
standard. The output from this analysis gave us statistical data to determine the
best practices for a mobile device testing environment.
D ’ Amato, D. (n.d.). Best practices for taking face photographs and face image quality metrics. (2006).
NIST Biometric Quality Workshop

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(Spring 2013) Best Practices for Facial Recognition using Mobile Devices

1. BEST PRACTICES FOR FACIAL RECOGNITION USING MOBILE DEVICES The purpose of this study was to determine the best practices and characteristics for capturing a quality image with a mobile device that meets standards for facial recognition software. The focus of our study on image quality revolved around lighting, subject pose, and camera properties. Our analysis of image quality was based off of ISO standards for facial recognition. Chris Nagelbach, James Sternberg, Cody Maus, William Payne, Michael Brockly, Stephen Elliot Overview Distance Angle Type Lighting 1.5 m, depth of field 10 cm 45 degrees left/right center, 35 degrees above center Dual Lighting, 800-900 Lux Pose/Person Eye width should be 25% of picture width and located at 60% of total image height +/- 5 degree from center Neutral emotion with naturally open eyes, 0 motion blur Camera Between 0.5 to 1 m +/- 5 degrees above/below eye level Back facing camera Background N/A N/A Illuminated 18% grey Best Practices Camera Distance From Face Eye Separation Compliant Failure Rate 0.5M 436.53 OK 0% 1.0M 182.48 OK 0% 1.5M 129.7 OK 0% 2.0M 104.52 Fail 100% Lighting Type Percent Facial Brightness Compliant Failure Rate Overhead Fluorescent Lighting 50.65 OK 7% 2 Studio Lamps at Eye Level 45˚ From Center 60.86 OK 0% 2 Studio Lamps 35˚ Above Eye level 45˚ from center 61.13 OK 0% Percent Background Gray Compliant Failure Rate Percent Facial Brightness Compliant Failure Rate Eye Separation Compliant Failure Rate Rear facing camera with white background 12.65673772 OK 0% 47.3 OK 0% 477.9623327 OK 0% Front facing camera with white background 8.808231499 OK 0% 34.7 Fail 30% 122.5834791 Fail 10% Rear facing camera with 18% grey background 42.66904296 OK 0% 62.9 OK 0% 607.0340979 OK 0% Front facing camera with 18% grey background 39.84486436 OK 0% 57.6 Ok 0% 121.4669226 Fail 20% Conclusions Camera Distance From Face Lighting Camera and Background Ideal Experiment Setup Based on our research and experimentations we have determined the ideal best practices listed in the best practices table above. We came to this conclusion by taking 10 pictures for each of the desired variables on an iPhone 4s. The pictures were processed against the ISO_FRONTAL_Best_Practices standard. The output from this analysis gave us statistical data to determine the best practices for a mobile device testing environment. D ’ Amato, D. (n.d.). Best practices for taking face photographs and face image quality metrics. (2006). NIST Biometric Quality Workshop

(Spring 2013) Best Practices for Facial Recognition using Mobile Devices

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