Face Detection and Recognition Using OpenCV and Vision Transformer

Face recognition technology is vital in the real world with diverse applications. It is primarily used for security, law enforcement, personalization, healthcare, and education. Face recognition systems use biometric features like facial landmarks, texture, and shape to identify and verify individuals. The suggested approach employs a transformer-based architecture that solely relies on self-attention and does not utilize Convolutional Layers. This design choice enables the model to be trained efficiently with minimal computational power and fewer parameters than a CNN. The application of Vision Transformer (ViT) in various computer vision tasks has been highly successful, making it a state-of-the-art approach. Given its superior performance, we are interested in exploring whether ViT can enhance the accuracy of sheep face recognition.In this paper, we show that ViT can be a useful technique for facial recognition. Since there was no predefined dataset for face recognition, a PCI dataset was built for this investigation. Along with the PCI dataset, two more well-known datasets, AT&T and 5_Celebrity, we used to examine performance. In our model was seen that ViT could identify human faces on the PCI dataset with a 99% accuracy rate and perform much better than other face recognition algorithms like Eigenface, FisherFace, and LBPH.