Comparison Between Deep Learning Models and Traditional Machine Learning Approaches for Facial Expression Recognition in Ageing Adults

Facial expression recognition is one of the most active areas of research in computer vision since one of the non-verbal communication methods by which one understands the mood/mental state of a person is the expression of face. Thus, it has been used in various fields such as human-robot interaction, security, computer graphics animation, and ambient assistance. Nevertheless, it remains a challenging task since existing approaches lack generalizability and almost all studies ignore the effects of facial attributes, such as age, on expression recognition even though the research indicates that facial expression manifestation varies with age. Recently, a lot of progress has been made in this topic and great improvements in classification task were achieved with the emergence of deep learning methods. Such approaches have shown how hierarchies of features can be directly learned from original data, thus avoiding classical hand designed feature extraction methods that generally rely on manual operations with labelled data. However, research papers systematically exploring the performance of existing deep architectures for the task of classifying expression of ageing adults are absent in the literature. In the present work a tentative to try this gap is done considering the performance of three recent deep convolutional neural networks models (VGG-16, AlexNet and GoogLeNet/Inception V1) and evaluating it on four different benchmark datasets (FACES, Lifespan, CIFE, and FER2013 ) which also contain facial expressions performed by elderly subjects. As the baseline, and with the aim of making a comparison, two traditional machine learning approaches based on handcrafted features extraction process are evaluated on the same datasets. Carrying out an exhaustive and rigorous experimentation focused on the concept of "transfer learning", which consists of replacing the output level of the deep architectures considered with new output levels appropriate to the number of classes (facial expressions), and training three different classifiers (i.e., Random Forest, Support Vector Machine and Linear Regression), VGG-16 deep architecture in combination with Random Forest classifier was found to be the best in terms of accuracy for each dataset and for each considered age-group. Moreover, the experimentation stage showed that the deep learning approach significantly improves the baseline approaches considered, and the most noticeable improvement was obtained when considering facial expressions of ageing adults.