Literature Survey on Face Recognition with Hybrid Deep Learning

Abstract

These Face recognition has made remarkable progress with the advent of deep learning techniques. However, accuracy and robustness are still critical for real-world applications. This survey paper explores the synergy between traditional and deep learning methods, providing a comprehensive analysis of hybrid deep learning models for face recognition. We first discuss the foundational techniques in traditional face recognition, such as eigenfaces, local binary patterns (LBP), and histogram of oriented gradients (HOG). These methods laid the groundwork for subsequent developments. We then introduce convolutional neural networks (CNNs), Siamese networks, and FaceNet, which are deep learning models that automate feature extraction from raw facial data.We also discuss the advantages and disadvantages of traditional and deep learning methods, as well as the challenges of hybrid deep learning models. Finally, we present an overview of the state-of-the-art hybrid deep learning models for face recognition. Focus of this survey is the concept of hybridization, where traditional and deep features harmoniously coexist. We provide a detailed examination of key hybrid models, such as DeepID, VGG-Face, and SphereFace, elucidating their architectures, components, and contributions to the field. Additionally, we delve into the integration of face detection and alignment techniques within hybrid models, underlining their significance in achieving accurate and standardized recognition. This paper also presents the latest literature on the Hybrid face recognition models and the techniques used. The paper highlights the advantages of hybrid models, including enhanced robustness, improved accuracy, and computational efficiency, while acknowledging challenges such as data requirements, computational resources, and ethical considerations. It concludes by underscoring the promising future of hybrid deep learning models in elevating the performance and responsible deployment of face recognition systems across various domains, from security and surveillance to human-computer interaction. This survey not only encapsulates the state-of-the-art but also beckons researchers and practitioners to delve deeper into the evolving landscape of face recognition with hybrid deep learning models.