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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).