In this paper we evaluate the combination of hand-crafted and deep learning-based features for neonatal pain assessment. To this end we consider two hand-crafted descriptors, i.e. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG), and features extracted from two pre-trained Convolutional Neural Networks (CNNs). Experimental results on the publicly available Infant Classification Of Pain Expressions (COPE) database show competitive results compared to previous methods.
Celona, L., Manoni, L. (2017). Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features. In New Trends in Image Analysis and Processing – ICIAP 2017 (pp.197-204). Springer Verlag [10.1007/978-3-319-70742-6_19].
Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features
Celona, L
Primo
;
2017
Abstract
In this paper we evaluate the combination of hand-crafted and deep learning-based features for neonatal pain assessment. To this end we consider two hand-crafted descriptors, i.e. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG), and features extracted from two pre-trained Convolutional Neural Networks (CNNs). Experimental results on the publicly available Infant Classification Of Pain Expressions (COPE) database show competitive results compared to previous methods.
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