Precise Face Recognition through GWO-Cuckoo Optimized Neural Networks and MRMR Feature Selection from Compressed Hybrid Domain Fusion

Abstract

In the domain of computer vision, face recognition systems play a pivotal role, particularly in the context of security and access control. This research paper introduces a novel approach to face recognition that leverages the fusion of compressed hybrid domain grades at a matching level. It takes into consideration two distinct compressed hybrid domain algorithms, referred to as algorithm one and algorithm two, with the aim of enhancing accuracy across ORL, YALE, and JAFFE face databases. In algorithm one, a combination of histogram intensities, discrete wavelet transform (DWT), and double density dual tree discrete wavelet transform (DDDTDWT) is employed for feature extraction. Notably, this algorithm also segments the binary bits of image pixel decimal values into most significant bits (MSB) and least significant bits (LSB). DWT is subsequently applied to the MSB, while the histogram of oriented gradients (HOG) is used for the LSB matrices. Meanwhile, Algorithm two explores a distinct approach by utilizing the GIST descriptor concept on the LL-sub band of DWT and HOG features, which are later combined through convolution. Moreover, for the neural network classifier, a GWO-Cuckoo optimized architecture is employed, ensuring that the extracted features are optimally utilized for precise face recognition. In addition, the minimum redundancy maximum relevance (MRMR) feature selection method is integrated into the feature extraction process. This further enhances the discriminative power of the selected features. The results achieved through this proposed technique are highly promising. Specifically, for the ORL, YALE, and JAFFE databases, accuracy rates of 97.71%, 100%, and 100% are achieved, respectively, surpassing the performance of existing methods.