An explainable fast deep neural network for emotion recognition

Facial expressions are the foundation of human non-verbal communication. A smile, a look of surprise, an expression of disgust—each emotion is conveyed through subtle facial micro-movements that, until recently, were difficult for machines to interpret with precision. Today, thanks to an innovative deep neural network, not only can emotions be recognized with high accuracy, but this can now be done quickly and, most importantly, in an explainable manner.

Deep neural networks have always faced a major challenge: their “black box” nature. While they make highly accurate predictions, their decision-making processes have long remained obscure. This new model overcomes this barrier through the use of explainable AI, which can identify and prioritize the most relevant features for emotion recognition. By integrating Integrated Gradients, an advanced Explainable AI technique, the model can analyze the contribution of each facial reference point (landmark) in classifying emotions. The original input consists of 468 facial landmarks extracted from video sequences, but through data relevance analysis, the system can reduce the number of utilized points without compromising accuracy. Tests have shown that by reducing the landmarks to 128, the model maintains an accuracy above 97% for certain emotions, such as surprise and happiness, while significantly lowering computational costs.

Optimizing the number of features is not just a theoretical exercise; it leads to tangible improvements in model performance. Tests conducted on standard datasets such as CK+ have revealed a significant reduction in inference time, making the system suitable for real-time applications.

The applications of this technology go far beyond basic facial recognition. In the medical field, a more precise analysis of facial expressions can support neurological and psychiatric diagnoses by detecting early signs of emotional disorders. In security systems, real-time monitoring of facial expressions can help identify suspicious intent, contributing to safer public spaces. The optimization of AI models through explainability techniques represents a major leap forward: not only does it ensure more reliable and transparent functioning, but it also paves the way for a future where AI is not just intelligent but also comprehensible and accessible.