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We present ongoing work to harness biological approaches to achieving highlyefficient egocentric perception by combining the space-variant imagingarchitecture of the mammalian retina with Deep Learning methods. Bypre-processing images collected by means of eye-tracking glasses to control thefixation locations of a software retina model, we demonstrate that we canreduce the input to a DCNN by a factor of 3, reduce the required number oftraining epochs and obtain over 98% classification rates when training andvalidating the system on a database of over 26,000 images of 9 object classes.

Efficient Egocentric Visual Perception Combining Eye-tracking, a Software Retina and Deep Learning