Face Recognition using Convolutional Neural Network in Machine Learning

Our sense of ourselves is inextricably linked to our looks. It’s required for everyday interactions, communication, and other routine duties. Face recognition algorithms that are both durable and perfect are required to construct fully automated systems that analyse the data contained in face photographs, and a variety of methodologies are currently being used. Partial facial occlusion is one of the most difficult challenges in face recognition. In real-world applications, face recognition algorithms can recognize faces hidden under masks, scarves, or sunglasses, hands on the face, things carried by a person, or external sources. The outcome, when compared to other existing algorithms, produces the best results. When utilising the suggested dataset, they provide high accuracy and a low loss function. With both trainable and non-trainable parameters, the suggested model performs admirably. The above-average accuracy of 80% indicates a strong performance in facial recognition. Face recognition from video and photos is extremely important.