Detection of facial emotions using Neuromorphic Computation

Modern computing systems are good for tasks that are difficult for humans, even for high-performance computing. Automation and artificial intelligence combined are disciplines, which emit to humans, generating structured data in real-time and transferable. This combination of systems is also compatible with new neuromorphic processor architectures. Neuromorphic computing is completely redesigned in its architecture to the conventional computer model, both in hardware and software. The Procedure that was carried out was the collection of faces with some emotion, the information was also generated for the real-life database, in which the classification of faces, and emotions was carried out using the theory of Robert Plutchik and with an artificial neural network spike, when combined with the previous concepts and tools, signals are obtained, which as a human would act and that does not work like a conventional computer. The result obtained from the classification of faces and emotions was made in a neural network whose performance is affected only when the contrast falls below 3%, it was also found that some images were 0.005%. Regarding noise resistance, the images withstood 50% noise, in which the performance of the network was not affected. Although neuromorphic computing is willing to simulate the human brain using artificial biological neural networks, it is also used in the classification of objects, and pattern recognition, with proposed image processing techniques, obtaining acceptable performance.