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Imaging of facial affects may be used to measure psychophysiologicalattributes of children through their adulthood, especially for monitoringlifelong conditions like Autism Spectrum Disorder. Deep convolutional neuralnetworks have shown promising results in classifying facial expressions ofadults. However, classifier models trained with adult benchmark data areunsuitable for learning child expressions due to discrepancies inpsychophysical development. Similarly, models trained with child data performpoorly in adult expression classification. We propose domain adaptation toconcurrently align distributions of adult and child expressions in a sharedlatent space to ensure robust classification of either domain. Furthermore, agevariations in facial images are studied in age-invariant face recognition yetremain unleveraged in adult-child expression classification. We takeinspiration from multiple fields and propose deep adaptive FACial Expressionsfusing BEtaMix SElected Landmark Features (FACE-BE-SELF) for adult-child facialexpression classification. For the first time in the literature, a mixture ofBeta distributions is used to decompose and select facial features based oncorrelations with expression, domain, and identity factors. We evaluateFACE-BE-SELF on two pairs of adult-child data sets. Our proposed FACE-BE-SELFapproach outperforms adult-child transfer learning and other baseline domainadaptation methods in aligning latent representations of adult and childexpressions.

Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features