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.

面部表情识别是计算机视觉研究中最活跃的领域之一，因为一种可以理解人的情绪/心理状态的非语言交流方法之一就是面部表情。因此，它已用于各种领域，例如人机交互，安全性，计算机图形动画和环境辅助。然而，由于现有方法缺乏可概括性，并且几乎所有研究都忽略了面部属性（例如年龄）对表情识别的影响，尽管这项研究表明面部表情的表现会随年龄而变化，但这仍然是一项艰巨的任务。最近，随着深度学习方法的出现，在该主题上已经取得了很多进展，并且在分类任务上取得了很大的进步。这样的方法显示了如何直接从原始数据中学习特征的层次结构，从而避免了通常依赖于带有标记数据的手动操作的经典手工设计的特征提取方法。但是，在文献中却没有研究论文系统地研究现有的深层结构的性能，以对衰老的成年人的表情进行分类。在目前的工作中，考虑到三个最新的深层卷积神经网络模型（VGG-16，AlexNet和GoogLeNet / Inception V1）的性能，并在四个不同的基准数据集（FACES，Lifespan，CIFE，和FER2013），其中还包含老年受试者的面部表情。作为基准，并且为了进行比较，在相同的数据集上评估了两种基于手工特征提取过程的传统机器学习方法。针对“转移学习”的概念进行了详尽而严格的实验，包括用适合于班级数量（面部表情）的新输出级别替换考虑的深度架构的输出级别，并训练三个不同的分类器（例如，随机森林，支持向量机和线性回归），VGG-16深层结构与随机森林分类器相结合，对于每个数据集和每个考虑的年龄组而言，在准确性方面都是最好的。此外，实验阶段表明，深度学习方法显着改善了所考虑的基准方法，