Action unit detection in infants relative to adults presents unique challenges. To detect facial action units in spontaneous behavior of infants, we propose a multi-label Convolutional Neural Network (CNN). Eighty-six infants were recorded during tasks intended to elicit enjoyment and frustration. The multi-label CNN achieved comparable agreement with manual coding. Kappa ranged from 0.69 to 0.93. Importantly, the CNN-based AU detection revealed the same change in findings with respect to infant expressiveness between tasks. While further research is needed, these findings suggest that automatic AU detection in infants is a viable alternative to manual coding of infant facial expression.