FPGA-based enhanced probabilistic convergent weightless network for human iris recognition

This paper investigates how human identification and identity verification can be performed by the application of an FPGA based weightless neural network, entitled the Enhanced Probabilistic Convergent Neural Network (EPCN), to the iris biometric modality. The human iris is processed for feature vectors which will be employed for formation of connectivity, during learning and subsequent recognition. The pre-processing of the iris, prior to EPCN training, is very minimal. Structural modifications were also made to the Random Access Memory (RAM) based neural network which enhances its robustness when applied in real-time.