On the search for efficient face recognition algorithm subject to multiple environmental constraints
From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test imag...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024045997 |
| _version_ | 1797235531954782208 |
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| author | John K. Essel Joseph A. Mensah Eric Ocran Louis Asiedu |
| author_facet | John K. Essel Joseph A. Mensah Eric Ocran Louis Asiedu |
| author_sort | John K. Essel |
| collection | DOAJ |
| description | From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively. |
| first_indexed | 2024-04-24T16:49:27Z |
| format | Article |
| id | doaj.art-486625901bbb4025bcb4773b08a8c231 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T16:49:27Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-486625901bbb4025bcb4773b08a8c2312024-03-29T05:50:43ZengElsevierHeliyon2405-84402024-04-01107e28568On the search for efficient face recognition algorithm subject to multiple environmental constraintsJohn K. Essel0Joseph A. Mensah1Eric Ocran2Louis Asiedu3C. K. Tedam University of Technology and Applied Sciences, Navrongo, Upper East Region, GhanaDepartment of Computer Science, Ashesi University, No. 1 University Avenue, Berekuso Eastern Region GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, Ghana; Corresponding author.From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively.http://www.sciencedirect.com/science/article/pii/S2405844024045997Histogram equalisationGamma correctionMultiple imputationMultiple constraintsPrincipal component analysisFaceNet algorithm |
| spellingShingle | John K. Essel Joseph A. Mensah Eric Ocran Louis Asiedu On the search for efficient face recognition algorithm subject to multiple environmental constraints Heliyon Histogram equalisation Gamma correction Multiple imputation Multiple constraints Principal component analysis FaceNet algorithm |
| title | On the search for efficient face recognition algorithm subject to multiple environmental constraints |
| title_full | On the search for efficient face recognition algorithm subject to multiple environmental constraints |
| title_fullStr | On the search for efficient face recognition algorithm subject to multiple environmental constraints |
| title_full_unstemmed | On the search for efficient face recognition algorithm subject to multiple environmental constraints |
| title_short | On the search for efficient face recognition algorithm subject to multiple environmental constraints |
| title_sort | on the search for efficient face recognition algorithm subject to multiple environmental constraints |
| topic | Histogram equalisation Gamma correction Multiple imputation Multiple constraints Principal component analysis FaceNet algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024045997 |
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