hlbao/facial-expression-recognition-with-keras

Deep learning based facial expression recognizer. Recognizes 6 expresions

Facial Expression Recognizer

####1.Background

VGG 16 architecture is used to classify 6 emotions.

• Paper on VGG architecture: Very Deep Convolutional Networks for Large-Scale Image Recognition

• Paper on which this project is based on: Recognizing Facial Expressions Using Deep Learning

• Other references: Convolutional Neural Network

• Techniques used in training:

  1. Class balancing using Resampling of Dataset

     Reference: 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset

  2. Data augmentation

     Reference:Data augmentation

2.Requirements

pip install -r requirements.txt

3.Use

• Use train_in_colab.ipynb to train the model in google colab
• Use model_from_weights.py to create model from weights
• Use realtime.py for realtime emotion recognition
• vgg.py is architecture definition using Keras

4. Results

Training:

• Loss : 0.9238
• Accuracy: 0.6496

Validation:

• Loss: 0.8733
• Accuracy: 0.6800

Test:

• Loss: 0.95643
• Accuracy: 0.6461

Output