The document summarizes a study on the effects of different lighting conditions on facial expression recognition software. It describes how the researchers took video clips of 3 facial expressions (neutral, joy, anger) under 5 lighting scenarios using male and female subjects. The analysis found that lighting from below generally produced the lowest accuracy ratings. Joy expressions were easiest for the software to detect. While some extreme lighting scenarios affected the data, more moderate real-world lighting is unlikely to have as strong an impact.
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Actionable outputs from capability assessments - project challengeILX Group
Mike Saville, ILX’s Head of Consulting explores how organisations can benefit from combining Best Practice and Organisational Capability Models (including P3m3 v3). This enables leadership teams to answer not only ‘how good are our projects?’, but to establish solutions to persistent problems and build the capability that they need. These themes are illustrated by real-world case studies from multi-national businesses.
Actionable outputs from capability assessments - project challengeILX Group
Mike Saville, ILX’s Head of Consulting explores how organisations can benefit from combining Best Practice and Organisational Capability Models (including P3m3 v3). This enables leadership teams to answer not only ‘how good are our projects?’, but to establish solutions to persistent problems and build the capability that they need. These themes are illustrated by real-world case studies from multi-national businesses.
1. The Effects of
Lighting Conditions on
Facial Encoding
Software
Lynn, Ben, Ashley, Allison, Jaein
2. Our Study
u We wanted to study the effects of different lighting
angles on the facial encoding software’s accuracy
u We chose to take short clips of 3 main facial expressions
o Neutral, Joy, and Anger
u In 5 main lighting scenarios
o Neutral, left, right, below and above spotlights
o Using lamps and small flashlights to enact these lighting situations
3. Advantages to this method
u Took videos so that the software could gather an accurate
average of the data and give a general reading rather
than using a picture
u We used a neutral light setting to gauge how the software
would normally rate these expressions
u Used both male and female subjects to ensure expression
analysis wasn’t gender specific
4. Disadvantages to this method
u Facial expressions may not be consistent across lighting
scenarios and may naturally alter the data we receive
u Facial expressions aren’t “natural” and the software may
pick up on that and skew the data
20. Findings & Conclusion
u Most of our analyses showed that lighting below the subjects
will have the lowest accuracy rating compared to the other
lighting scenarios
u Joy was the easiest for the software to detect so the ratings
were generally higher across all lighting scenarios
u Our study highlighted the some extreme lighting scenarios
and in a real-time study, it would most likely not affect the
data
o it is something to keep in mind when analyzing data going forward
