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| # Face-Detection | ||
| Face detection (class and psoe) using various Classifiers | ||
|
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| ## Author | ||
|
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| ### Siddharth Telang (stelang@umd.edu) | ||
|
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| ## Subject Code | ||
| ### CMSC828C/ENEE633 Project 1 | ||
|
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||
| ## 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: | ||
|
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||
| - 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 | ||
|
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||
| ### 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 | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,66 @@ | ||
| Siddharth Telang | ||
| 116764520 | ||
| stelang@umd.edu | ||
|
|
||
| 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 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 | ||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
| python adaboost.py -dataset Data/data.mat -subjects 200 -types 2 -trainingSize 300 -pca True -kernel linear - iterations 10 |