Similar research had been done before, but there the test subjects were shown a set of movie clips, instead of a whole movie. The authors claim that showing a whole movie results in "more naturalistic" data.
The problem is a classification task with two classes: "face" and "non-face". It was solved using ordinary least squares (OLS) for regression. However, since there was a lot of data, OLS couldn't be used in the conventional way. The prediction was done using only a subset of the features, which were selected using prior information and different methods, resulting in four regression models:
- Stepwise Regression (SWR)
- Simulated Annealing (SA)
- Least Absolute Shrinkage and Selection Operator (LASSO)
- Least Angle Regression (LARS)
Out of which LASSO and LARS are regulated to be sparse, possibly resulting in less overfitting.
Figure: The best prediction acquired with LARS compared to the (roughly binary) annotation. As can be seen, binary prediction (1 when > 0.5) would match the annotation well. Also locations of 6 features in three bain regions visualized.
Human brain is divided into different regions with different tasks. This study provided a natural way (at least for a computer scientist) to find out which regions are associated with face recognition, and thus, can be used in the prediction. In their paper, the authors say, "our results support the view that face detection is distributed across the visual cortex, albeit the fusiform cortex has a strong influence on face detection."