The human face is viewed as the mirror that reflects the inner feelings of the person and allows us to detect the needs of every person and study his behaviour and requirements. We can also identify the tastes of each person and predict his reaction through facial features. Indeed, the detection, classification, characterization of the face are research areas that have received considerable interest in the recent twenty years. However, most published works in this field study the emotions for a person in an upright posture, which means that the person must perfectly face the camera. The innovation in the present work is to improve the detection of emotions in the case of different face orientations (to the right, left, up and down). Eight-teen feature points that perfectly characterize the human face are firstly calculated. An optimization step is proposed by extracting a set of optimal distances between the facial points as a new set of optimized emotion descriptors. For this reason, we have used a statistical characterization criterion based on the ratio of the intra-class variance. An emotion classification experiment is carried out using a multilayer neural network implemented with a digital signal processing microcontroller.