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