For the past decades, face recognition (FR) has been actively studied in computer vision and pattern recognition society. Recently, due to the advances in deep learning, the FR technology shows high performance for most of the benchmark datasets. However, when the FR algorithm is applied to a real-world scenario, the performance has been known to be still unsatisfactory. This is mainly attributed to the mismatch between training and testing sets. Among such mismatches, face misalignment between training and testing faces is one of the factors that hinder successful FR. To address this limitation, we propose a face shape-guided deep feature alignment framework for FR robust to the face misalignment. Based on a face shape prior (e.g., face keypoints), we train the proposed deep network by introducing alignment processes, i.e., pixel and feature alignments, between well-aligned and misaligned face images. Through the pixel alignment process that decodes the aggregated feature extracted from a face image and face shape prior, we add the auxiliary task to reconstruct the well-aligned face image. Since the aggregated features are linked to the face feature extraction network as a guide via the feature alignment process, we train the robust face feature to the face misalignment. Even if the face shape estimation is required in the training stage, the additional face alignment process, which is usually incorporated in the conventional FR pipeline, is not necessarily needed in the testing phase. Through the comparative experiments, we validate the effectiveness of the proposed method for the face misalignment with the FR datasets.

翻译：在过去几十年中,在计算机视野和模式识别社会积极研究了面部识别(FR),最近,由于深层次学习的进步,FR技术显示大多数基准数据集的高级性能。然而,当FR算法应用到现实世界情景时,业绩仍然不令人满意。这主要归因于培训和测试组合之间的不匹配。在这种不匹配中,培训和测试面部之间的不匹配是阻碍成功FR的因素之一。为了应对这一限制,我们建议为FR提供一个面部对面调整的面部引导深度特征调整框架。基于之前的面部形状(例如,面对关键点),我们通过引入对齐进程,即对相近和特征调整,对拟议深度网络进行培训。在对面图像和面面面部进行解码的像校正进程中,我们加上了对面面面面面面部调整的辅助任务。由于在前面面面面面面面面面面面面面面面面图像的合并特征与前面面面面面面部提取(例如平比值阶段的测试,我们要求的图像校正的模拟,我们要求的面面面面面面面面面面面面面面部的校正的校正,因此,我们需要将面面面面面面面面面面面面面面面面面面面面面面部的校正的校正的校正的校正的校正的校正,我们的校正,因此,我们的校正,我们的整过程是我们的校正的校正的校正的校正,我们的校路路路路路路路路路路路路路路路,我们的校路路路路路路路路路路的合并的合并的合并的合并,我们通常路路路路路路路路路路路路路路路路路路路路路路路路路路路的合并的合并的合并的合并的合并的合并的合并的合并的合并的合并的合并,通常路路通常的合并,我们通常路路路路路路路路路路通常的合并的合并路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路