Facial recognition system image by analyzing multiple facial regions and details. It consists of a non-linear regression model that maps a specific thermal image into a corresponding visible facial image and an optimization issue that projects the latent projection back into the image space. ARL scientists have noted that the approach works by combining global information (i.e. features across the entire face) with local information (i.e. features regarding the eyes, nose, and mouth). In addition to enhancing the discriminability of the synthesized image, the facial recognition system can be used to transform a thermal face signature into a refined visible image of a
Facial coding features of face (mouth, eyebrows etc.) and analyzes movement, shape and texture composition of these regions to identify facial action units. Therefore, it is possible to track tiny movements of facial muscles in individuals’ face and translate them into universal facial expressions that convey happiness, surprise, sadness, anger and others. An algorithm developed by EyeSee can detect the following seven emotions: happiness, surprise, puzzle, disgust, fear, sadness and neutral. The results of facial coding provide insight into viewers’ spontaneous, unfiltered reactions to visual content, by recording and automatically analyzing facial expressions during the process. It enables moment-by-moment emotional and cognitive