iCub Facial Recognition On Track

Partly due to the increasing use of social robots, scientists today are keen on advancing facial recognition software. In order to do this, researchers have been working on  models of natural visual systems; however, turning the effortless intricacies of the cerebral cortex into hard wires and code is tricky business. A lot of facial recognition software  systems are based on a large dictionary of features stored in memory that must be filtered by artificial neurons through out various layers. Through these means, invariance to factors such as position and scale can be achieved but it’s often at the high cost of increasing the number of connections between the layers of the network which results in bulkier hardware.

To tackle this issue, researchers from the SPECS lab at Pompeu Fabra University employed a model of an encoding scheme called the Temporal Population Code (TPC) in an artificial system. Applied as part of the facial recognition feature of the iCub robot, the TPC model has important advantages over many other models used in the past.  It is wire-independent, allowing the system to accommodate  various streams of complex information and it can also be used as a generic framework for object recognition tasks regardless of input. Beyond facial recognition, the TPC model can be generalized to other tasks such as hand writing recognition.

The iCub seems to be a fan of the TPC model. In the face recognition tasks, its  speed of encoding was compatible with the human visual system with a 97% sucess rate. These results are included in a paper titled:, "The encoding of complex visual stimuli by a canonical model of the primary visual cortex: temporal population coding for face recognition on the iCub robot."The research paper was one of the five finalists for best conference paper at the 2011 International Conference on Robotics and Biomimetics






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Talk about geekiness, here is another iCub video of the robot playing a Theramin: