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For the past decades, face recognition (FR) has been actively studied incomputer vision and pattern recognition society. Recently, due to the advancesin deep learning, the FR technology shows high performance for most of thebenchmark datasets. However, when the FR algorithm is applied to a real-worldscenario, the performance has been known to be still unsatisfactory. This ismainly attributed to the mismatch between training and testing sets. Among suchmismatches, face misalignment between training and testing faces is one of thefactors that hinder successful FR. To address this limitation, we propose aface shape-guided deep feature alignment framework for FR robust to the facemisalignment. Based on a face shape prior (e.g., face keypoints), we train theproposed deep network by introducing alignment processes, i.e., pixel andfeature alignments, between well-aligned and misaligned face images. Throughthe pixel alignment process that decodes the aggregated feature extracted froma face image and face shape prior, we add the auxiliary task to reconstruct thewell-aligned face image. Since the aggregated features are linked to the facefeature extraction network as a guide via the feature alignment process, wetrain the robust face feature to the face misalignment. Even if the face shapeestimation is required in the training stage, the additional face alignmentprocess, which is usually incorporated in the conventional FR pipeline, is notnecessarily needed in the testing phase. Through the comparative experiments,we validate the effectiveness of the proposed method for the face misalignmentwith the FR datasets.

Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment