Pose-invariant face recognition via feature-space pose frontalization

• URL: http://arxiv.org/abs/2505.16412v1
• Date: Thu, 22 May 2025 09:01:01 GMT
• Title: Pose-invariant face recognition via feature-space pose frontalization
• Authors: Nikolay Stanishev, Yuhang Lu, Touradj Ebrahimi,
• Abstract summary: Pose-invariant face recognition is a challenging problem for modern AI-based face recognition systems.<n>In this paper, a new method is presented to perform face frontalization and recognition within the feature space.<n>New training paradigm is proposed to maximize the potential of FSPFM and boost its performance.
• Score: 9.105950041800225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pose-invariant face recognition has become a challenging problem for modern AI-based face recognition systems. It aims at matching a profile face captured in the wild with a frontal face registered in a database. Existing methods perform face frontalization via either generative models or learning a pose robust feature representation. In this paper, a new method is presented to perform face frontalization and recognition within the feature space. First, a novel feature space pose frontalization module (FSPFM) is proposed to transform profile images with arbitrary angles into frontal counterparts. Second, a new training paradigm is proposed to maximize the potential of FSPFM and boost its performance. The latter consists of a pre-training and an attention-guided fine-tuning stage. Moreover, extensive experiments have been conducted on five popular face recognition benchmarks. Results show that not only our method outperforms the state-of-the-art in the pose-invariant face recognition task but also maintains superior performance in other standard scenarios.

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