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 lnood/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.oil expression recognition even though the research indicates that facial expression inanifestation 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 sliown 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 witli labelled data.However,research papers systematically exploring tlu4 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 convolutiorial neural networks models(VGG-16,AlexNet and GoogLeNet/Inception V1)and evaluating it on four different benchmark datcisets(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 inacliine 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 Macliine and Linear Regression),VGG-16 deep architecture in combination with Random Forest classifier was found to be the best in terms of accuracy for eacli dataset and for each considered age-group,Moreover,the experinientation stage showed that the deep learning approacli significantly improves the baseline approaches considered,and the most noticeable improvement was obtained when considering facial expressions of ageing adults.