This article proposes a novel framework for the recognition of six universal facial expressions. The framework is based on three set of features extracted from the face image: entropy, brightness and local binary pattern. First, saliency maps are obtained by state-of-the-art saliency detection algorithm i.e. “frequency-tuned salient region detection”. The idea is to use saliency maps to find appropriate weights or values for extracted features (i.e. brightness and entropy). To validate the performance of saliency detection algorithm against human visual system, we have performed a visual experiment. Eye movements of 15 subjects were recorded with an eye-tracker in free viewing conditions as they watch a collection of 54 videos selected from Cohn-Kanade facial expression database. Results of the visual experiment provided the evidence that obtained saliency maps conforms well with human fixations data. Finally, evidence of the proposed framework’s performance is exhibited through satisfactory classification results on Cohn-Kanade database, FG-NET FEED database and Dartmouth database of children’s faces.