This study presents an innovative approach to enhance facial recognition technology using contrastive convolutional neural networks (CNNs). The primary focus is on improving the accuracy and efficiency of face recognition systems under varying conditions. Key elements of this approach include meticulous data preparation and preprocessing, where images undergo normalization and diverse augmentation techniques to ensure quality inputs. The network architecture is designed to process pairs of face images, utilizing a common feature extractor and cascaded convolution layers for detailed feature representation. A specialized kernel generator further refines the process, emphasizing unique facial characteristics. The core of the training regimen is a contrastive loss function, optimized through gradient descent to enhance the network's discriminatory capabilities. Results from the study demonstrate a significant improvement in recognition accuracy, particularly highlighted by the superior performance of the proposed model in comparison to standard facial recognition algorithms. This research provides a comprehensive methodology that could revolutionize face recognition technology, offering more reliable and efficient solutions for various applications.
