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Recognizes emotion using webcam (live). (In Progress)

HTML 12.78% CSS 4.11% Jupyter Notebook 73.60% Shell 2.09% Python 4.88% JavaScript 2.54%

cnn computer-vision css deep-learning emotion-recognition facial-expression-recognition keras machine-learning

emotion-recognition's Introduction

Hi there 👋

I am Suyog Jadhav, a master's student at UiT Norway.

🔭 I’m currently researching on theoretical aspects of computer vision.
🎓 I’m currently pursuing a master's in computer science.
📖 I'm looking forward to learning the mathematics behind machine learning.
💻 Apart from deep learning, I also take interest in speedcubing, graphic design, photography, 3D pen art and other creative endeavors.
📫 Reach out to me @IAmSuyogJadhav on all major social platforms.
✒️ I occasionally write blogs on suyogjadhav.com
⚡ Fun fact: I once wrote a piece of code that worked without any errors right away.

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emotion-recognition's Issues

Biased predictions

There seems to be a high bias towards Neutral and Happiness classes. A possible reason might be the class imbalance. Try to investigate the class distribution and oversample deficient classes.

Class Imbalance seems to be the main cause of this issue. Whenever a dataset contains a lot of training examples from one class and very few from some of the other classes, the model we train over it tends to be biased towards the class with high no. of examples.

Possible solutions:

Use the same dataset used by us (mentioned in the README) to train the model and use a weighted loss function that gives high weight for correctly classifying minority class.
Use a bigger dataset (for e.g., AffectNet, consisting of close to 1M images) and use sampling techniques to oversample^ minority classes / undersample^^ majority classes.

^Oversampling: Increasing the no. of class examples by various methods, including duplication, flipping, rotating, randomly zooming etc.
^^Undersampling: Decreasing the no. of majority class examples.

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