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Large scale training of Deep Learning methods requires significant computational resources. The use of transfer learning methods tends to speed up learning while producing complex networks that are very hard to interpret. This paper investigates the use of a low-complexity image processing system to investigate the advantages of using AM-FM representations versus raw images for face detection. Thus, instead of raw images, we consider the advantages of using AM, FM, or AM-FM representations derived from a low-complexity filterbank and processed through a reduced LeNet-5. The results showed that there are significant advantages associated with the use of FM representations. FM images enabled very fast training over a few epochs while neither IA nor raw images produced any meaningful training for such low-complexity network. Furthermore, the use of FM images was 7x to 11x faster to train per epoch while using 123x less parameters than a reduced-complexity MobileNetV2, at comparable performance (AUC of 0.79 vs 0.80). 

We introduce a new method to detect student group interactions in collaborative learning videos. We consider the following video activities: (i) human to human, (ii) human to others, and (iii) lack of any interaction. The system uses multidimensional AM-FM methods to detect student faces, hair, and then use the results to detect possible interactions. We use dynamic graphs to represent group interactions within each video.We tested our methods with 15 videos and achieved an 84% accuracy for students facing the camera and 76% for students facing both towards and away from the camera.