By using deep learning-based strategy, the performance of face recognition tasks has been significantly enhanced. However, the verification and discrimination of the faces with occlusions still remain a challenge to most of the state-of-the-art approaches. Bearing this in mind, we propose a novel convolutional neural network which was designed specifically for the verification between the occluded and non-occluded faces for the same identity. It could learn both the shared and unique features based on a multiple network convolutional neural network architecture. The newly presented joint loss function and the corresponding alternating minimization approach were integrated to implement the training and testing of the presented convolutional neural network. Experimental results on the publicly available datasets (LFW 99.73%, YTF 97.30%, CACD 99.12%) show that the proposed deep representation approach outperforms the state-of-the-art face verification techniques.

中文翻译：

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深度表示用于部分遮挡的面部验证

通过使用基于深度学习的策略，人脸识别任务的性能得到了显着提高。然而，对遮挡脸部的验证和区分仍然是大多数最新技术的挑战。考虑到这一点，我们提出了一种新颖的卷积神经网络，该网络是专门为验证同一身份的被遮挡脸和未被遮挡脸而设计的。它可以基于多网络卷积神经网络体系结构学习共享和独特的功能。集成了新提出的关节损失函数和相应的交替最小化方法，以实现对提出的卷积神经网络的训练和测试。公开数据集上的实验结果（LFW 99.73％，YTF 97.30％，CACD 99。