Developing and Applying a Hybrid Machine Learning Model for Sturdy Face Recognition

Abstract

Face recognition plays a vital role in a wide range of security and personal identification applications—from surveillance systems to biometric authentication. Although recent advancements have greatly improved performance, current face recognition models still struggle with real-world challenges like variations in lighting, facial pose, age, and occlusion. In this paper, we present a hybrid machine learning approach that combines Convolutional Neural Networks (CNNs) for powerful feature extraction with Support Vector Machines (SVMs) for reliable classification. This combination is designed to improve both the accuracy and robustness of face recognition systems in diverse, real-world settings. We tested our model on well-known datasets such as Labeled Faces in the Wild (LFW) and VGGFace2, and the results show that our hybrid method consistently outperforms traditional models in terms of accuracy, precision, recall, and resilience to changes in facial features and conditions. These findings suggest that the proposed system is highly suitable for demanding applications like surveillance, border security, and other biometric identification tasks.

Introduction

This study focuses on developing a hybrid face recognition system that combines Convolutional Neural Networks (CNNs) for deep feature extraction with Support Vector Machines (SVMs) for classification. Traditional face recognition methods (like PCA and LDA) struggle under real-world conditions such as lighting changes, pose variations, aging, and occlusions. CNNs improved performance but still face challenges with extreme variations.

The hybrid model leverages CNNs to learn complex facial features and uses SVMs, which excel in high-dimensional, non-linear classification, to enhance accuracy and robustness. The model is trained and evaluated on large, diverse datasets (LFW and VGGFace2), with preprocessing steps including face detection, alignment, and augmentation to handle variations.

Experiments show the hybrid approach performs better than traditional methods, maintaining strong accuracy and generalization under varying conditions. However, it still faces limitations with heavily occluded faces or extreme lighting and requires further testing on diverse real-world data.

Conclusion

In this study, we designed and tested a hybrid machine learning model for face recognition that leverages the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Support Vector Machines (SVMs) for classification. The results showed that this hybrid approach offers enhanced accuracy and robustness compared to traditional methods, achieving strong, state-of-the-art performance on benchmark datasets. Looking ahead, future research will aim to further boost the model’s ability to generalize by integrating other advanced machine learning techniques and testing it on a wider range of diverse, real-world datasets.

References

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Copyright

Copyright © 2025 Deepak Chauhan, Himanshu Vashistha, Dev Sharma, Mr. Harendra Singh, Dr. Sureshwati , Dr. Saumya Chaturvedi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.