An ensemble face recognition mechanism based on three-way decisions

The explainable human-computer interaction (HCI) is about designing approaches capable of using cog-nitive characteristics like humans. One such characteristic is human vision and its accuracy. The accuracy measures the trust in that system. Therefore, improving accuracy in the authorization with identification process is a primary concern for a visual-based explainable human-computer interaction system. In this article, we propose a three-way decision based ensembled face recognition mechanism called E3FRM. The E3FRM uses a three-way approach to determine the match cases and the respective worth of the captured image with the match cases. Features are extracted using PCA/FLD, and the ensembled face recognition algorithms utilize the extracted features to process the image. Ensemble Face recognition approaches find the match cases based on a given threshold. Finally, the three-way decision model evaluates the suitabil-ity of the captured image for acceptance, rejection, or deferred cases with a dual verification mechanism. Experimental results on well-known eighteen datasets suggest improvements in commonly used metrics of F1, Accuracy and Recall by up to 0.8% to 12.8%, 1% to 9.6% and 1.2% to 13.9%, respectively, in compar-ison to the state-of-the-art methods available, including SPCA +, ML-EM, FLDA-SVD, DMMA, Fast-DMMA, LU, LPP, TDL, KCFT, RBF + DT, and NMF. Furthermore, the proposed approach is comparatively analyzed with ensembled face recognition methods that result in an outperformed F1, Accuracy and Recall by up to 1.1% to 10.3%, 0.1% to 7.3% and 0.9% to 10.5%, respectively. These results suggest that the proposed model may improve face recognition accuracy and the resulting trust in the machines.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This article introduces a three-way decision mechanism called E3FRM, which utilizes human visual characteristics to improve accuracy and trust in visual-based explainable human-computer interaction systems. Experimental results show that E3FRM outperforms existing methods in F1, Accuracy, and Recall by up to 12.8%, 9.6%, and 13.9%, respectively. Therefore, this model has the potential to enhance face recognition accuracy and trust in machines.