Illumination-Recovered Pose Normalization for Unconstrained Face Recognition

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• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10113
• 期：1
• 页码：217-233
• 丛书名：Computer Vision ? ACCV 2016
• ISBN：978-3-319-54187-7
• 卷排序：10113

文摘

Identifying subjects with pose variations is still considered as one of the most challenging problems in face recognition, despite the great progress achieved in unconstrained face recognition in recent years. Pose problem is essentially a misalignment problem together with self-occlusion (information loss). In this paper, we propose a continuous identity-preserving face pose normalization method and produce natural results in terms of preserving the illumination condition of the query face, based on only five fiducial landmarks. “Raw” frontalization is performed by aligning a generic 3D face model into the query face and rendering it at frontal pose, with an accurate self-occlusion part estimation based on face borderline detection. Then we apply Quotient Image as a face symmetrical feature which is robust to illumination to fill the self-occlusion part. Natural normalization result is obtained where the self-occlusion part keeps the illumination conditions of the query face. Large scale face recognition experiments on LFW and MultiPIE achieve comparative results with state-of-the-art methods, verifying effectiveness of proposed method, with advantage of being database-independent and suitable both for face identification and face verification.