Seven Basic Expression Recognition Using ResNet-18
This work addresses expression recognition for affective computing, but it is incremental as it applies an existing method to a new dataset with minor modifications.
The paper tackled the problem of classifying seven basic expressions in affective behavior analysis in-the-wild using a ResNet-18 model, achieving an ABAW2 score of 0.4, which exceeded the competition baseline.
We propose to use a ResNet-18 architecture that was pre-trained on the FER+ dataset for tackling the problem of affective behavior analysis in-the-wild (ABAW) for classification of the seven basic expressions, namely, neutral, anger, disgust, fear, happiness, sadness and surprise. As part of the second workshop and competition on affective behavior analysis in-the-wild (ABAW2), a database consisting of 564 videos with around 2.8M frames is provided along with labels for these seven basic expressions. We resampled the dataset to counter class-imbalances by under-sampling the over-represented classes and over-sampling the under-represented classes along with class-wise weights. To avoid overfitting we performed data-augmentation and used L2 regularisation. Our classifier reaches an ABAW2 score of 0.4 and therefore exceeds the baseline results provided by the hosts of the competition.