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