An ensemble face recognition mechanism based on three-way decisions
The explainable human–computer interaction (HCI) is about designing approaches capable of using cognitive 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 ide...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-04-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823000848 |
| _version_ | 1797837792059850752 |
|---|---|
| author | Anwar Shah Bahar Ali Masood Habib Jaroslav Frnda Inam Ullah Muhammad Shahid Anwar |
| author_facet | Anwar Shah Bahar Ali Masood Habib Jaroslav Frnda Inam Ullah Muhammad Shahid Anwar |
| author_sort | Anwar Shah |
| collection | DOAJ |
| description | The explainable human–computer interaction (HCI) is about designing approaches capable of using cognitive 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 suitability 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 comparison 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. |
| first_indexed | 2024-04-09T15:31:26Z |
| format | Article |
| id | doaj.art-a2656233e1324ce9960a6942b0d7065c |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-09T15:31:26Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-a2656233e1324ce9960a6942b0d7065c2023-04-28T08:54:13ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-04-01354196208An ensemble face recognition mechanism based on three-way decisionsAnwar Shah0Bahar Ali1Masood Habib2Jaroslav Frnda3Inam Ullah4Muhammad Shahid Anwar5Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Peshawar Campus, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanDepartment of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 01026, Zilina, Slovakia; Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, 70800, Ostrava, Czech RepublicDepartment of Computer Engineering, Gachon University, Seongnam, Sujeong-gu 13120, Republic of KoreaDepartment of AI and Software Gachon University, 13120 Seongnam-si, Republic of Korea; Corresponding author.The explainable human–computer interaction (HCI) is about designing approaches capable of using cognitive 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 suitability 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 comparison 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.http://www.sciencedirect.com/science/article/pii/S1319157823000848Deep FaceFace RecognitionE3FRMEnsembleThree- way ClusteringThree-way Decisions |
| spellingShingle | Anwar Shah Bahar Ali Masood Habib Jaroslav Frnda Inam Ullah Muhammad Shahid Anwar An ensemble face recognition mechanism based on three-way decisions Journal of King Saud University: Computer and Information Sciences Deep Face Face Recognition E3FRM Ensemble Three- way Clustering Three-way Decisions |
| title | An ensemble face recognition mechanism based on three-way decisions |
| title_full | An ensemble face recognition mechanism based on three-way decisions |
| title_fullStr | An ensemble face recognition mechanism based on three-way decisions |
| title_full_unstemmed | An ensemble face recognition mechanism based on three-way decisions |
| title_short | An ensemble face recognition mechanism based on three-way decisions |
| title_sort | ensemble face recognition mechanism based on three way decisions |
| topic | Deep Face Face Recognition E3FRM Ensemble Three- way Clustering Three-way Decisions |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823000848 |
| work_keys_str_mv | AT anwarshah anensemblefacerecognitionmechanismbasedonthreewaydecisions AT baharali anensemblefacerecognitionmechanismbasedonthreewaydecisions AT masoodhabib anensemblefacerecognitionmechanismbasedonthreewaydecisions AT jaroslavfrnda anensemblefacerecognitionmechanismbasedonthreewaydecisions AT inamullah anensemblefacerecognitionmechanismbasedonthreewaydecisions AT muhammadshahidanwar anensemblefacerecognitionmechanismbasedonthreewaydecisions AT anwarshah ensemblefacerecognitionmechanismbasedonthreewaydecisions AT baharali ensemblefacerecognitionmechanismbasedonthreewaydecisions AT masoodhabib ensemblefacerecognitionmechanismbasedonthreewaydecisions AT jaroslavfrnda ensemblefacerecognitionmechanismbasedonthreewaydecisions AT inamullah ensemblefacerecognitionmechanismbasedonthreewaydecisions AT muhammadshahidanwar ensemblefacerecognitionmechanismbasedonthreewaydecisions |