Unleash the Power of Deep Neural Networks and Transfer Learning for Enhanced Face Recognition

Abstract

Face recognition has become a crucial technology with diverse applications in various domains such as biometric security, healthcare, personal identification, and law enforcement. It stands out due to its high recognition rate, the richness of features, its universality, and its uniqueness. Having said that, challenges such as varying illumination, poses, facial expressions, face transformations, and backgrounds have hindered recognition accuracy, especially when using traditional techniques that rely on explicit feature extraction before the actual classification. The emergence of deep learning algorithms, such as Convolutional Neural Networks (CNNs), has exhibited prominent potential in image recognition capabilities, but building effective face recognition systems from scratch demands substantial labeled data and computational resources.

To overcome these challenges, this paper proposes a novel approach that utilizes transfer learning and a pre-trained AlexNet CNN to improve face recognition capabilities. The model is trained with three different deep-learning optimizers, and the most suitable one is selected for the proposed system. For experiments, we created a custom database with images of five distinct individuals, generating over 1260 face images with each person having at least 250 samples. The dataset was further enriched using data augmentation technique. Using MATLAB with the Deep Learning Toolbox, the results demonstrate the model's recognition rate, peaking up to 100 per cent when the SGD optimizer is employed, 97.88 per cent when RMSProp is the optimizer, and 99.2 per cent for the Adam optimizer while using the same set of hyperparameters. These findings showcase the effectiveness of the approach and open doors for more efficient and accurate face recognition systems in real-world scenarios with large datasets.

https://doi.org/10.59200/ICARTI.2023.012