  Face Recognition is one of the prominent problems in the computer vision
domain. Witnessing advances in deep learning, significant work has been
observed in face recognition, which touched upon various parts of the
recognition framework like Convolutional Neural Network (CNN), Layers, Loss
functions, etc. Various loss functions such as Cross-Entropy, Angular-Softmax
and ArcFace have been introduced to learn the weights of network for face
recognition. However, these loss functions do not give high priority to the
hard samples as compared to the easy samples. Moreover, their learning process
is biased due to a number of easy examples compared to hard examples. In this
paper, we address this issue by considering hard examples with more priority.
In order to do so, We propose a Hard-Mining loss by increasing the loss for
harder examples and decreasing the loss for easy examples. The proposed concept
is generic and can be used with any existing loss function. We test the
Hard-Mining loss with different losses such as Cross-Entropy, Angular-Softmax
and ArcFace. The proposed Hard-Mining loss is tested over widely used Labeled
Faces in the Wild (LFW) and YouTube Faces (YTF) datasets. The training is
performed over CASIA-WebFace and MS-Celeb-1M datasets. We use the residual
network (i.e., ResNet18) for the experimental analysis. The experimental
results suggest that the performance of existing loss functions is boosted when
used in the framework of the proposed Hard-Mining loss.
