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aam
aamvec
au
emo
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README.md
README.md~
aam_au.ipynb
aam_emotions.ipynb
aam_emotions2.ipynb
ahakone_paper.pdf
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au_emotion2.ipynb
zscore.py

README.md

Comparing Supervised Machine Learning Algorithms for Emotion Recognition Performance

Description

I compared 8 machine learning algorithms for human emotion recognition using Scikit-learn's python library. I used the Extended Cohn-Kanade Dataset (CK+), which is a database developed by Carnegie Mellon Univer- sity and the University of Pittsburgh, and it contains facial expression data that can be used to study automatic facial expression detection. Specifically, I used the Active Appearance Models Landmarks (AAMs), Facial Action Coding System (FACS) Action Unit (AU) Labels, and the emotion labels for the video sequence.

Algorithms

  • Linear Discriminant Analysis (LDA)
  • Support Vector Machine (SVM)
  • Stochastic Gradient Descent (SGD)
  • K-Nearest Neighbors k = 5 (KNN5)
  • K-Nearest Neighbors k = 10 (KNN10)
  • K-Nearest Neighbors k = 15 (KNN15)
  • Naive Bayes (NB)
  • Decision Tree (DT)
  • AdaBoost (AB)
  • Label propagation (LP)

Features and Classification

  • AAM as feature, emotion label as classification
  • AU as feature, emotion label as classfication
  • AAM as feature, AU as classification

Results