Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
This study aims to develop a deep computational model which is a novel aggregation of fuzzy clustering fused with evolutionary searching and a neural network based on a proposed artificial neuron structure in complex domain. In our Complex Deep Intelligent System (CDIS), we propose a complex neural classifier built upon a new complex neuron structure ‘C-TROIKA’. The proposed deep model which is an amalgamation of Fused Fuzzy Distribution (FFD) and Complex Neural Classifier (CNC) capitulates an efficient tool for human recognition. The functional aptitudes of conventional neurons have been explored with complex-valued non-linear aggregation functions. This aggregation has the ability to confine higher-order correlations among input patterns. The proposed neuron structure based on these aggregation functions enables the system to provide faster convergence, better learning and recognition accuracy. The effectiveness and strengths of proposed complex neuron structure ‘C-TROIKA’ based deep intelligent system have been demonstrated over three benchmark biometric datasets, CASIA iris, Yale face and Indian face to realize the motivation.
How to cite this article:
Swati Srivastava and Bipin Kumar Tripathi. Deep Intelligent System for Human Recognition in Complex Domain.
DOI: https://doi.org/10.36478/jeasci.2019.373.385
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2019.373.385