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Face-Detection

Face detection (class and psoe) using various Classifiers. Dimentionality Reduction using PCA and MDA

Classifiers implemented from scratch

  • Bayes
  • kNN
  • SVM (Linear, RBF, Polynomial kernels)
  • AdaBoost with SVM

Author

Siddharth Telang (stelang@umd.edu)

Subject Code

CMSC828C/ENEE633 Project 1

Programming language used: Python3+

Dependencies (to be installed through pip):

1) sklearn(used only for PCA): pip install sklearn
2) matplotlib: pip install matplotlib
3) numpy: pip install numpy
4) scipy (.mat to python): pip install scipy
5) cvxopt (quadratic solver): pip install cvxopt

Contents:

1) Code files:
- Helper functions (my_pca, my_mda, my_lda, helper_functions, svm_helper) used by main files
- bayes_classifier_subject, knn_subject: Subject label classification
- bayes_classifier: bayes' classifier implementation
- classify_pose_Bayes, classify_pose_KNN: Pose identification for Data set 1
- svm_classifier, adaboost: Pose identification for Data set 1
2) Report
3) Figures - all plots
4) Data folder containing the dataset

Steps to run the code:

  • Please ensure this to be the current working directory.
  • Various commands with different permutations are mentioned below.
  • You may use this on the command prompt and terminal.
  • A choice of choosing among pca or mda is provided. Feel free to update if required, only one of them can be set to True at a time.
  • trainingSize parameter can be altered to test on various training and testing size
  • kernel parameter is provided to select between 'rbf', 'poly', and 'linear' kerel svm
  • iterations parameter can be updated for more number of iterations in boosted svm

1) Subject Label classification

  • Bayes Classifier
python bayes_classifier_subject.py -dataset Data/data.mat -subjects 200 -types 3 -pca True
python bayes_classifier_subject.py -dataset Data/pose.mat -subjects 68 -types 13 -mda True
python bayes_classifier_subject.py -dataset Data/illumination.mat -subjects 68 -types 21 -mda True
  • k-NN
python knn_subject.py -dataset Data/data.mat -subjects 200 -types 3 -pca True
python knn_subject.py -dataset Data/pose.mat -subjects 68 -types 13 -mda True
python knn_subject.py -dataset Data/illumination.mat -subjects 68 -types 21 -mda True

2) Neutral vs Expression identification

  • Bayes Classifier
python classify_pose_Bayes.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 200 -pca True
python classify_pose_Bayes.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 200 -mda True
python classify_pose_Bayes.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 300 -mda True
  • k-NN
python classify_pose_KNN.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 200 -pca True
python classify_pose_KNN.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 200 -mda True
python classify_pose_KNN.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 100 -mda True
  • Kernel SVM
python svm_classifier.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 300 -pca True -kernel rbf 
python svm_classifier.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 300 -mda True -kernel rbf 
python svm_classifier.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 300 -pca True -kernel poly
python svm_classifier.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 300 -pca True -kernel linear
  • Ada-Boost (Linear SVM)
python adaboost.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 300 -pca True -kernel linear - iterations 10