Facial Action Units Recognition using Deep Learning Model with Bottleneck Features

Abstract

This paper presents a framework for detecting action units. The proposed framework consists of two frameworks: feature engineering for improving regression model accuracy and bottleneck features for improving deep learning model accuracy. For a regression framework, This paper presents an SVM with a feature formed by the feature engineering method, which consists of four features: histograms of directional gradients (HOG), facial landmarks, facial landmark distance from the center, and facial landmark angle relative to the center. For a deep learning framework, This paper presents AlexNet and VGG16 with bottleneck features and other optimization techniques such as hyperparameter tuning and image augmentation. Both frameworks presented were trained with the CK+ dataset. For the detection of 10 action units, the results showed that the deep learning framework outperformed the regression framework by 11%. For the detection of 17 action units, the results showed that AlexNet and VGG16 had the highest accuracy compared to other deep learning models, with an accuracy of 74%. Additionally, they outperform the linear SVC model and the Feed Forward MLP model by 10% and 12%, respectively.