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arXiv · 2301.01922

Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning

Hojin Park·Jaewoo Park·Andrew Beng Jin Teoh
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Abstract

In this paper, we focus on addressing the open-set face identification problem on a few-shot gallery by fine-tuning. The problem assumes a realistic scenario for face identification, where only a small number of face images is given for enrollment and any unknown identity must be rejected during identification. We observe that face recognition models pretrained on a large dataset and naively fine-tuned models perform poorly for this task. Motivated by this issue, we propose an effective fine-tuning scheme with classifier weight imprinting and exclusive BatchNorm layer tuning. For further improvement of rejection accuracy on unknown identities, we propose a novel matcher called Neighborhood Aware Cosine (NAC) that computes similarity based on neighborhood information. We validate the effectiveness of the proposed schemes thoroughly on large-scale face benchmarks across different convolutional neural network architectures. The source code for this project is available at: https://github.com/1ho0jin1/OSFI-by-FineTuning

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Hojin Park, Jaewoo Park, Andrew Beng Jin Teoh. 2023-01-05. Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning. https://doi.org/10.1109/icpr56361.2022.9956714

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