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Face recognition (FR) methods report significant performance by adopting theconvolutional neural network (CNN) based learning methods. Although CNNs aremostly trained by optimizing the softmax loss, the recent trend shows animprovement of accuracy with different strategies, such as task-specific CNNlearning with different loss functions, fine-tuning on target dataset, metriclearning and concatenating features from multiple CNNs. Incorporating thesetasks obviously requires additional efforts. Moreover, it demotivates thediscovery of efficient CNN models for FR which are trained only with identitylabels. We focus on this fact and propose an easily trainable and single CNNbased FR method. Our CNN model exploits the residual learning framework.Additionally, it uses normalized features to compute the loss. Our extensiveexperiments show excellent generalization on different datasets. We obtain verycompetitive and state-of-the-art results on the LFW, IJB-A, YouTube faces andCACD datasets.

DeepVisage: Making face recognition simple yet with powerful generalization skills