Boosting Facial Recognition Accuracy through Daubechies Wavelet Techniques and Deep Convolutional Models

Facial Recognition (FR) plays a crucial role in a variety of fields, such as surveillance, healthcare, security, and robotics. Although several facial recognition algorithms are available, many still struggle to deliver high accuracy and real-time performance due to inherent limitations. Recent breakthroughs in deep learning, especially with the implementation of advanced neural network architectures like Convolutional Neural Networks (CNNs), have significantly enhanced the effectiveness of facial recognition systems. This paper presents a novel system that integrates the Viola-Jones face detection algorithm, the Daubechies Discrete Wavelet Transform (DWT), and CNNs. This integrated approach significantly enhances recognition accuracy, achieving a ${9 8 \%}$ success rate and outperforming previous methods that relied on individual techniques for specific image sets.