Completed: Set up AWS instance Processed 2058 of each emotion and trained Accuracy around 50 to 60% Used log loss for SVM Todo: Balance emotions in train/test split Currently shuffled split may not be balanced Migrate code to AWS and expand EBS storage Train second classifier for contempt, disgust, and Read more…
Completed: Trained with 60000 images Balanced out images of each emotion Confusion matrix Accuracy of around 60-70 percent 7000 images – got around 80-90 percent but could be overfit Tested on my own face – contempt, anger, disgust, fear are confused Todo: Make second SVM for classifying hard to differentiate Read more…
This week we just continued working on our individual components of our project. Patrick continued training with images from AffectNet. Vinay continued training and working on the LSTM for sentiment analysis. Yoojin found a paper that detailed a CNN for tone recognition and collected the libraries and documentation to start Read more…
Completed: Trained with 10,000 images from AffectNet Accuracy still around 70% Lowest around 30% Highest at 77% Confusion matrix shows highest accuracy for happy, then anger, then sadness, surprise, fear, then contempt and disgust are not detected Downloaded 7 out of 11 parts of AffectNet so far To-do: Start batch Read more…
Completed: Personal SOW Downloaded AffectNet manually-annotated images Save trained SVM locally Testing different test/train splits Just testing and training further To-do: Train with more images Cross-validation on AffectNet Confusion matrix
Completed: Refocus SOW Setting up Zoom Migrating FER algorithm to home desktop computer Setting up PyCharm, Tensorflow, Keras, SkLearn, OpenCV Downloading datasets Reworking code to work in new environment To-do: Continue using more of AffectNet database Find more ways to improve accuracy Tweaking SVM parameters like bias, loss, etc Perform Read more…
This week we finished our design report and collected most of our parts for our hardware. We still need a screen and microphone. We need to set up an AWS server since we also got our AWS credits. We are still working on our individual components: training the sentiment analysis Read more…
Completed tasks: Test preliminary trained SVM Have SVM prediction in real-time Include AffectNet dataset in SVM training Design report To-do: Train with more AffectNet images Figure out how to output with probabilities for each emotion instead of single output Improve accuracy Set up AWS and train on server
Completed tasks: Automated I/O of CK+ dataset Localized pre-processed VGG19 features of CK+ dataset for faster training Preliminary training of SVM with 327 labeled pictures from CK+ dataset Working on evaluating trained SVM Implemented real-time prediction with camera input To-do: Evaluate performance of preliminary trained SVM Speed up real-time SVM Read more…
Completed tasks: Verified performance of VGG19 Working on PCA of features from 4th pooling layer of VGG19 100000 features to 200 principal components Still working on writing SVM To-do: Finish PCA and SVM algorithm Download CK+ database and begin preliminary training Design presentation and report