A big, diverse and balanced training data is the key to the success of deep neural network training. However, existing publicly available datasets used in facial landmark localization are usually much smaller than those for other computer vision tasks. A small dataset without diverse and balanced training samples cannot support the training of a deep network effectively. To address the above issues, this paper presents a novel Separable Batch Normalization (SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust facial landmark localization. Different from the standard BN layer that uses all the training data to calculate a single set of parameters, SepBN considers that the samples of a training dataset may belong to different sub-domains. Accordingly, the proposed SepBN module uses multiple sets of parameters, each corresponding to a specific sub-domain. However, the selection of an appropriate branch in the inference stage remains a challenging task because the sub-domain of a test sample is unknown. To mitigate this difficulty, we propose a novel attention mechanism that assigns different weights to each branch for automatic selection in an effective style. As a further innovation, the proposed CNT strategy trains a network using multiple datasets having different facial landmark annotation systems, boosting the performance and enhancing the generalization capacity of the trained network. The experimental results obtained on several well-known datasets demonstrate the effectiveness of the proposed method.
翻译:大型、多样化和均衡的培训数据是深层神经网络培训成功的关键。 但是,在面部标志性定位中,现有的公开数据集通常比用于其他计算机视觉任务的数据集要小得多。 没有多样化和均衡的培训样本的小型数据集无法有效支持深层网络的培训。 为了解决上述问题,本文件展示了一个具有跨方案网络强化定位化的跨方案网络培训战略的新型可分离批量标准化模块(SepBN) 。 与使用所有培训数据计算单一参数的BN层标准不同, SepBN认为培训数据集的样本可能属于不同的子域块。 因此,拟议的SepBN模块使用多种参数,每个参数对应特定的子域块。 然而,在判断阶段选择适当的分支仍然是一项具有挑战性的任务,因为测试样本的子域块未知。为了减轻这一困难,我们提议了一个新的关注机制,为每个分支分配不同重量来计算一套单一参数,SepBNB认为培训数据集的样本可能属于不同的子域块。因此,拟议的SepBNB模块模块使用多个测试数据系统,从而进一步提升了经过培训的系统。