Few-shot learning CNN optimized using combined 2D-DWT injection and evolutionary optimization algorithms for human face recognition

Abstract

Convolutional Neural Network (CNN) has shown remarkable success in the area of machine vision. The purpose of this research is to enhance the classification for the few-shot learning datasets by developing a robust feature extraction system using an optimized CNN model. The aforementioned goal is attained in the following way by developing two classification models, (1) CNN optimized using Two-Dimensional Discrete Wavelet Transform (2D-DWT) injection using Principal Component Analysis (PCA), and Grey Wolf Optimizer (GWO), and (2) CNN optimized using 2D-DWT injection using PCA and Multi-Verse Optimizer (MVO) algorithm. This optimization process enhances the rate of face recognition for the small training dataset by extracting maximum features. Experiments on the AT&T (ORL), LFW, and Extended Yale-FACE-B databases show that the technique improves results significantly, with recognition rates increasing to 100% for training accuracy on all datasets and 100%, 98%, 97% on ORL, LFW, and Extended Yale-FACE-B datasets, respectively, for testing accuracy.