Average life expectancy has increased steadily in recent decades. This phenomenon, considered together with aging of the population, will inevitably produce in the next years deep social changes that lead to the need of innovative services for elderly people, focused to improve the wellbeing and the quality of life. In this context many potential applications would benefit from the ability of automatically recognize facial expression with the purpose to reflect the mood, the emotions and also mental activities of an observed subject. Although facial expression recognition (FER) is widely investigated by many recent scientific works, it still remains a challenging task for a number of important factors among which one of the most discriminating is the age. In the present work an optimized Convolutional Neural Network (CNN) architecture is proposed and evaluated on two benchmark datasets (FACES and Lifespan …