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

• Published in: IEEE transactions on biometrics, behavior, and identity science behavior, and identity science, 2022-10, Vol.4 (4), p.1-1
• Main Authors: Kim, Hyung-Il, Yun, Kimin, Ro, Yong Man
• Format: Article
• Language: English
• Subjects: Algorithms; Alignment; Computer vision; Datasets; Estimation; face alignment; face alignment learning; Face recognition; face shape prior; Faces; Feature extraction; Hair preparations; Heating systems; Image reconstruction; Machine learning; Misalignment; multi-task learning; Pattern recognition; Pixels; Robustness; Shape; Training
• ISSN: 2637-6407; 2637-6407
• DOI: 10.1109/TBIOM.2022.3213845

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.