  Face recognition (FR) methods report significant performance by adopting the
convolutional neural network (CNN) based learning methods. Although CNNs are
mostly trained by optimizing the softmax loss, the recent trend shows an
improvement of accuracy with different strategies, such as task-specific CNN
learning with different loss functions, fine-tuning on target dataset, metric
learning and concatenating features from multiple CNNs. Incorporating these
tasks obviously requires additional efforts. Moreover, it demotivates the
discovery of efficient CNN models for FR which are trained only with identity
labels. We focus on this fact and propose an easily trainable and single CNN
based FR method. Our CNN model exploits the residual learning framework.
Additionally, it uses normalized features to compute the loss. Our extensive
experiments show excellent generalization on different datasets. We obtain very
competitive and state-of-the-art results on the LFW, IJB-A, YouTube faces and
CACD datasets.
