Shape-invariant encoding of dynamic primate facial expressions in human perception.

Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.

Using a model of human visual perception to improve deep learning.

Deep learning algorithms achieve human-level (or better) performance on many tasks, but there still remain situations where humans learn better or faster. With regard to classification of images, we argue that some of those situations are because the human visual system represents information in a format that promotes good training and classification. To demonstrate this idea, we show how occluding objects can impair performance of a deep learning system that is trained to classify digits in the MNIST database. We describe a human inspired segmentation and interpolation algorithm that attempts to reconstruct occluded parts of an image, and we show that using this reconstruction algorithm to pre-process occluded images promotes training and classification performance.