The performance of feature learning for deep convolutional neural networks (DCNNs) is increasing promptly with significant improvement in numerous applications. Recent studies on loss functions clearly describing that better normalization is helpful for improving the performance of face recognition (FR). Several methods based on different loss functions have been proposed for FR to obtain discriminative features. In this paper, we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented. In additive parameter approach, an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features. We train the model on publically available dataset CASIA-WebFace, and our experiments on famous benchmarks YouTube Faces (YTF) and labeled face in the wild (LFW) achieve better performance than the various state-of-the-art approaches.

中文翻译：

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用于深脸识别的附加参数

深度卷积神经网络（DCNN）的特征学习性能正在迅速提高，并且在许多应用程序中都有显着改进。最近关于损失函数的研究清楚地描述了更好的归一化有助于改善人脸识别（FR）的性能。已经提出了几种基于不同损失函数的方法来进行FR，以获得鉴别特征。在本文中，我们提出了一个依赖于乘积角余量的加性参数，以提高可以轻松实现的特征嵌入的判别能力。在加性参数方法中，以有角度的边缘种子的结果对幼苗元素进行自动调整，以特定的方式为有角softmax提供学习角度区分特征的方法。