Neonatal facial pain detection using NNSOA and LSVM
We report classification experiments using the pilot Infant COPE database of neonatal facial expressions. Two sets of DCT coeffiecents were used to train a neural network simultaneous algorithm (NNSOA) and a linear support vector machine (LSVM) to classify neonatal expressions into the two categories of pain and nonpain. In the first set (VAR) only 80 of the coefficients with the highest variance were included. In the second set (SFFS), 15 DCT coefficients were selected by applying Sequential Forward Floating Selection (SFFS). We found that NNSOA+VAR produced the best classification score of 95.38% accuracy, but with no statistical difference compared with the DCT sets. However, NNSOA using the DCT coefficients outperformed with statistical significance previous experiments reported in  that used PCA components. It is surmised that NNSOA, an algorithm that eliminates unnecessary weights, is more stable than LSVM and may be better than SFFS at identifying relevant features.
Information Technology and Cybersecurity
Face classification, Facial expression recognition, NNSOA, Pain detection, SFFS
Brahnam, Sheryl, Loris Nanni, and Randall S. Sexton. "Neonatal Facial Pain Detection Using NNSOA and LSVM." In IPCV, pp. 352-357. 2008.
Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008