Recognition of Neonatal Facial Expressions of Acute Pain Using Boosted Gabor Features

Facial expressions are considered a critical factor in neonatal pain assessment. This paper proposes a pain expression recognition method using boosted Gabor features. Each neonatal facial image is convoluted with the 2D Gabor filters to extract 412,160 Gabor features. Since the high-dimension Gabor feature vectors are quite redundant, we employs a modified version of AdaBoost algorithm to select and combine the most informative features for classification. The "pain vs. non-pain" problem is treated as two sub-problems by using a coarse-to-fine hierarchical classifier. Experiments with 510 neonatal expression images show that the proposed method is quite effective. Only 30 Gabor features are enough to achieve good classification performance. The recognition rate of pain versus non-pain is up to 88% (i.e. error rate isin=0.12). Compared with one existing algorithm for neonatal facial pain recognition,our approach can reach similar accuracy in much lower time complexity.