Robust Face Recognition Using Deep Learning and Ensemble Classification
Facial recognition systems are widely used in various applications such as security, healthcare, and authentication, but face significant challenges in uncontrolled environments. Poor lighting conditions can obscure facial features, introduce shadows, and distort spatial relationships, while changes...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11018371/ |
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| author | Pavani Chitrapu Mahesh Kumar Morampudi Hemantha Kumar Kalluri |
| author_facet | Pavani Chitrapu Mahesh Kumar Morampudi Hemantha Kumar Kalluri |
| author_sort | Pavani Chitrapu |
| collection | DOAJ |
| description | Facial recognition systems are widely used in various applications such as security, healthcare, and authentication, but face significant challenges in uncontrolled environments. Poor lighting conditions can obscure facial features, introduce shadows, and distort spatial relationships, while changes in pose are critical for accurate identification. Existing methods often struggle to strike a balance between accuracy, computational efficiency, and robustness. Deep learning has become popular for automatically learning features through convolution layers. This study proposes a robust framework that integrates contrast-limited adaptive histogram equalization (CLAHE) and adaptive gamma correction for illumination normalization and multi-task cascaded convolutional networks (MTCNN) for precise face detection under varying poses and lighting conditions. This study proposes a deep learning-based approach for face recognition utilising multiple models, including VGG16, VGG19, ResNet-50, ResNet-101, and MobileNetV2. For classification, an ensemble of SVM, XGBoost, and random forest classifiers is combined using weighted averaging. The approach is tested on datasets such as CASIA3D and 105PinsFace, which include variations in illumination conditions. Using deep learning for automated hierarchical feature extraction and ensemble strategies, experimental results demonstrate significant improvements in recognition accuracy and enhanced robustness against lighting and pose variations while ensuring scalability for real-world applications. The approach achieved 99.91% accuracy on the CASIA3D dataset and 98.77% on the 105PinsFace dataset, showcasing its effectiveness across challenging conditions. |
| format | Article |
| id | doaj-art-f3ce2a8ad3ff4104a1ee85bfe59008cc |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f3ce2a8ad3ff4104a1ee85bfe59008cc2025-08-20T02:22:56ZengIEEEIEEE Access2169-35362025-01-0113999579996910.1109/ACCESS.2025.357519211018371Robust Face Recognition Using Deep Learning and Ensemble ClassificationPavani Chitrapu0Mahesh Kumar Morampudi1Hemantha Kumar Kalluri2https://orcid.org/0000-0003-1938-9098Department of CSE, SRM University-AP, Neerukonda, Amaravati, Andhra Pradesh, IndiaDepartment of CSE, SRM University-AP, Neerukonda, Amaravati, Andhra Pradesh, IndiaDepartment of CSE, SRM University-AP, Neerukonda, Amaravati, Andhra Pradesh, IndiaFacial recognition systems are widely used in various applications such as security, healthcare, and authentication, but face significant challenges in uncontrolled environments. Poor lighting conditions can obscure facial features, introduce shadows, and distort spatial relationships, while changes in pose are critical for accurate identification. Existing methods often struggle to strike a balance between accuracy, computational efficiency, and robustness. Deep learning has become popular for automatically learning features through convolution layers. This study proposes a robust framework that integrates contrast-limited adaptive histogram equalization (CLAHE) and adaptive gamma correction for illumination normalization and multi-task cascaded convolutional networks (MTCNN) for precise face detection under varying poses and lighting conditions. This study proposes a deep learning-based approach for face recognition utilising multiple models, including VGG16, VGG19, ResNet-50, ResNet-101, and MobileNetV2. For classification, an ensemble of SVM, XGBoost, and random forest classifiers is combined using weighted averaging. The approach is tested on datasets such as CASIA3D and 105PinsFace, which include variations in illumination conditions. Using deep learning for automated hierarchical feature extraction and ensemble strategies, experimental results demonstrate significant improvements in recognition accuracy and enhanced robustness against lighting and pose variations while ensuring scalability for real-world applications. The approach achieved 99.91% accuracy on the CASIA3D dataset and 98.77% on the 105PinsFace dataset, showcasing its effectiveness across challenging conditions.https://ieeexplore.ieee.org/document/11018371/Face recognitiondeep learningCLAHEMTCNNensembling |
| spellingShingle | Pavani Chitrapu Mahesh Kumar Morampudi Hemantha Kumar Kalluri Robust Face Recognition Using Deep Learning and Ensemble Classification IEEE Access Face recognition deep learning CLAHE MTCNN ensembling |
| title | Robust Face Recognition Using Deep Learning and Ensemble Classification |
| title_full | Robust Face Recognition Using Deep Learning and Ensemble Classification |
| title_fullStr | Robust Face Recognition Using Deep Learning and Ensemble Classification |
| title_full_unstemmed | Robust Face Recognition Using Deep Learning and Ensemble Classification |
| title_short | Robust Face Recognition Using Deep Learning and Ensemble Classification |
| title_sort | robust face recognition using deep learning and ensemble classification |
| topic | Face recognition deep learning CLAHE MTCNN ensembling |
| url | https://ieeexplore.ieee.org/document/11018371/ |
| work_keys_str_mv | AT pavanichitrapu robustfacerecognitionusingdeeplearningandensembleclassification AT maheshkumarmorampudi robustfacerecognitionusingdeeplearningandensembleclassification AT hemanthakumarkalluri robustfacerecognitionusingdeeplearningandensembleclassification |