SmileDetector — A New Approach to Live Smile Detection

https://github.com/NoahCristino/SmileDetector

Motivation and Goal

Dataset

GENKI-4000 Dataset

Data Processing

DLib applied to face

Network Structure

SVC Model Diagram

Additional Features

video processed by model

Future Applications

Mood Analysis

Conclusion

Our project resulted in a much faster model than competing smile detection models while maintaining high accuracy. The speed allowed us to perform a live video analysis up to 720 FPS, allowing us to analyze webcam footage in real-time. This makes it possible to use our model for applications previously not possible with other competing models. Furthermore, we can analyze higher resolution cameras in real-time and run our model alongside others due to its small size. This allows us to generate additional data that can be used alongside the user’s emotion for analyzing engagement. Shrinking the number of required resources to create a faster model has allowed us to use this technology in new ways and outperform existing models.

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University of Toronto Machine Intelligence Team

University of Toronto Machine Intelligence Team

UTMIST’s Technical Writing Team publishes articles on topics within machine learning to our official publication: https://medium.com/demistify