Innovative Multi-Face Recognition Framework Leveraging Hybrid Convolutional Neural Network Architectures

Abstract

In the contemporary epoch characterized by the relentless progression of technology and the ubiquitous presence of big data, the deployment of facial recognition systems has witnessed a remarkable surge across diverse domains. Among the various approaches, deep learning-based convolutional neural networks (CNNs) have garnered substantial attention and have been increasingly adopted for their exceptional performance in facial recognition tasks. Traditional facial recognition methods often necessitate intricate and time-consuming feature extraction procedures, which are highly dependent on domain expertise and may suffer from limited generalization capabilities. In stark contrast, CNN-based facial recognition offers a paradigm shift. It obviates the need for elaborate manual feature engineering by leveraging the network's inherent capacity to automatically learn hierarchical and discriminative features from raw facial images. Specifically, in the practical implementation of CNN-based facial recognition, the OpenCV library plays a pivotal role in the initial stage of face detection. OpenCV provides a set of robust and efficient algorithms for accurately identifying facial regions within complex image scenes, thereby serving as a crucial preprocessing step. Subsequently, the detected facial images are fed into the CNN architecture for training. Through an iterative optimization process, the CNN model automatically adjusts its internal parameters to minimize the recognition error, ultimately resulting in a well-trained and feasible network model.