Deep Convolution Neural Network Based Face Recognition

Face recognition is a critical and challenging task in the field of computer vision and biometrics, with applications ranging from security and surveillance to human-computer interaction. In recent years, deep learning techniques, particularly deep convolutional neural networks (CNNs), have revolutionized the face recognition landscape by achieving remarkable accuracy and robustness. It is a means of enhancing the quality of Object recognition; it plays a crucial role in various computer vision applications, such as autonomous driving, surveillance systems, and image retrieval. Deep Convolutional Neural Networks (CNNs) have emerged as a powerful approach for achieving state-of-the-art results in object recognition tasks. The core of this paper details the architecture and design of the proposed deep CNN-based face recognition system. It covers key components such as data preprocessing, network architecture selection, training strategies, and optimization techniques. An evaluation of various CNN architectures, including FaceNet, VGGNet, FACENet, ResNet, and EfficientNet, is presented along with their respective strengths and weaknesses in the context of face recognition. The research was aimed at creating an effective system that can accurately recognize student faces and display corresponding profile from the database before examination. FaceNet is the preferred CNN architecture for this work as it has comparatively better accuracy than other CNN architectures. It explores the challenges posed by variations in pose, illumination, and occlusion, and discusses techniques employed to mitigate these challenges within the proposed system. Additionally, the paper included fine-tuning strategies for adapting a pre-trained model to specific face recognition tasks. In creating this hardware, the model was pre-trained with student faces for recognition. 50 individuals, each with 20 images, were imputed as the dataset, giving a total of 1000 images in the dataset. These 1000 images were used to test the various popular CNN models given above. The test resulted in: VGGNet having an accuracy of 92%, precision of 90%, recall of 91%, and inference speed of 0.5: ResNet 50 having an accuracy of 93%, precision of 91%, recall of 92%, and inference speed of 0.3: MobileNet having an accuracy of 88, precision of 84, recall of 86, and inference speed of 0.2: FaceNet having an accuracy of 94%, precision of 92%, recall of 93, % and inference speed of 0.4. The resulted showed that optimized models designed for edge AI microprocessors significantly enhanced inference speed while maintaining similar levels of accuracy, as demonstrated by the FaceNet model.