Proceedings Article | 1 May 2017
Proc. SPIE. 10203, Pattern Recognition and Tracking XXVIII
KEYWORDS: Facial recognition systems, Detection and tracking algorithms, Image processing, Pattern recognition, Robotics, Feature extraction, Computer vision technology, Machine vision, Object recognition, Neural networks, Optical character recognition, Performance modeling, Network architectures, Neurons, Brain
Large-scale feed-forward neural networks have seen intense application in many computer vision problems.
However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our
work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based
hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks
such as maze traversal and image processing when compared to generic feed forward networks. While deep neural
networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical
structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in
SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time,
potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial
expression and character recognition tasks using novel deep recurrent model and compares recognition performance
with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed
deep SRN based recognition framework in a humanoid robotic platform called NAO.
