Swati Srivastava

Work place: Department of Computer Science & Engineering, Harcourt Butler Technical University, Kanpur, India

E-mail: swati.cs1409@gmail.com


Research Interests: Computational Mathematics, Computer systems and computational processes, Computational Learning Theory, Computer Vision


Swati Srivastava is pursuing her Ph.D in Computational Intelligence from HBTU Kanpur, India and completed M.Tech in Computer Science from NIT Allahabad, India. She is currently research scholar in the Department of Computer Science and Engineering of HBTU Kanpur, India. Her areas of research include high-dimensional neurocomputing, computational intelligence, machine learning and computer vision focused on biometrics.

Author Articles
Development of Hybrid Learning Machine in Complex Domain for Human Identification

By Swati Srivastava Bipin K. Tripathi

DOI: https://doi.org/10.5815/ijisa.2019.01.06, Pub. Date: 8 Jan. 2019

This paper presents a hybrid learning machine for human identification. It is a merger of eigenface with fisherface method, genetic fuzzy clustering and complex neural network. The non-linear aggregation based summation and radial basis function neural networks (NLA-SRBF NNs) are proposed as one of the functional component of the novel learning machine. The architecture of NLA-SRBF NNs incorporates hidden neurons, with summation and radial basis aggregation, and output neurons with only summation aggregation, along with complex resilient propagation (ÄŚRPROP) learning procedure. The improved learning and speedy convergence of NLA-SRBF NN enables the hybrid machine to provide better recognition accuracy. The learning machine consists of feature extraction, unsupervised clustering and supervised classification module. The aim of our proposal is to enhance the performance of biometric based recognition system. The efficacy and potency of our hybrid learning machine demonstrated on three benchmark biometric datasets-extended Cohn-Kanade, FERET and AR face datasets to comprehend the motivation. The performance comparisons of different variations of hidden neuron and learning algorithm thoroughly presented the superiority of the proposed NN based hybrid learning machine.

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