CosFace is a novel deep face recognition method that introduces the Large Margin Cosine Loss (LMCL) to enhance the discriminative power of face features. The method reformulates the traditional softmax loss into a cosine loss by normalizing both feature vectors and weight vectors, removing radial variations. This allows the model to maximize the decision margin in the angular space, leading to improved inter-class separation and reduced intra-class variance. The proposed CosFace model achieves state-of-the-art performance on several benchmark datasets, including LFW, YTF, and MegaFace. The key idea of CosFace is to maximize the cosine margin between different classes, which is achieved by introducing a margin term in the cosine loss function. This approach ensures that features from different classes are separated in the angular space, while features from the same class are clustered together. The model is trained using the LMCL loss function, which is designed to enhance the discriminative power of learned features by enforcing a large margin between different classes. Compared to other loss functions such as A-Softmax, CosFace offers a more consistent and effective margin in the cosine space, leading to better performance in face recognition tasks. The model is evaluated on several popular face recognition datasets, and it achieves superior results in terms of both face verification and identification accuracy. The proposed approach is also theoretically analyzed, showing that the cosine margin helps in achieving better discriminative power by focusing on the angular space rather than the Euclidean space. The experiments demonstrate that CosFace outperforms existing methods in terms of accuracy and robustness, particularly in challenging scenarios with large-scale face recognition. The model is trained on a variety of datasets, including both small and large-scale datasets, and shows consistent performance across different benchmarking scenarios. The results indicate that the proposed approach is effective in learning highly discriminative face features, making it a promising method for face recognition applications.CosFace is a novel deep face recognition method that introduces the Large Margin Cosine Loss (LMCL) to enhance the discriminative power of face features. The method reformulates the traditional softmax loss into a cosine loss by normalizing both feature vectors and weight vectors, removing radial variations. This allows the model to maximize the decision margin in the angular space, leading to improved inter-class separation and reduced intra-class variance. The proposed CosFace model achieves state-of-the-art performance on several benchmark datasets, including LFW, YTF, and MegaFace. The key idea of CosFace is to maximize the cosine margin between different classes, which is achieved by introducing a margin term in the cosine loss function. This approach ensures that features from different classes are separated in the angular space, while features from the same class are clustered together. The model is trained using the LMCL loss function, which is designed to enhance the discriminative power of learned features by enforcing a large margin between different classes. Compared to other loss functions such as A-Softmax, CosFace offers a more consistent and effective margin in the cosine space, leading to better performance in face recognition tasks. The model is evaluated on several popular face recognition datasets, and it achieves superior results in terms of both face verification and identification accuracy. The proposed approach is also theoretically analyzed, showing that the cosine margin helps in achieving better discriminative power by focusing on the angular space rather than the Euclidean space. The experiments demonstrate that CosFace outperforms existing methods in terms of accuracy and robustness, particularly in challenging scenarios with large-scale face recognition. The model is trained on a variety of datasets, including both small and large-scale datasets, and shows consistent performance across different benchmarking scenarios. The results indicate that the proposed approach is effective in learning highly discriminative face features, making it a promising method for face recognition applications.