An accelerated approach for facial expression analysis on video files

Automatic analysis and classification of facial expressions is a challenging problem which takes the attention of researchers studying in many areas such as human-computer interaction, computer vision and image processing. currently, especially due to developments in human-computer interaction, the understanding of human emotions by computer has become an indispensable issue. besides, analysis and recognition of facial expressions has prevailed in various areas like psychology, security, health, entertainment, and robotics. for these reasons, the analyzing of facial expressions quickly and correctly play a critical role for many software systems in different applications. in this study, an approach is proposed for the accelerated facial expression analysis of video files. facial expressions are considered in four class; happy, normal, confused and sad. by reducing the total number of analyzed video frames and using parallel threads, performance evaluation of the expression analysis accelerated on multi-core computer was presented. experimental results were obtained using quad-core processor with hyper threading technology. according to experimental results, quad core processor using two threads produced about 1.8 fold speed-up and four threads produced about 3 fold speed-up while eight threads produced about 3.5 fold speed-up has been obtained. additionally, the results of image frames which were found to be incorrect were fixed by performing error analysis on the results of statistical analysis.