FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912189294575616 |
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| author | Kahya, Sabri Mustafa Sivrikaya, Boran Hamdi Yavuz, Muhammet Sami Steinbach, Eckehard |
| author_facet | Kahya, Sabri Mustafa Sivrikaya, Boran Hamdi Yavuz, Muhammet Sami Steinbach, Eckehard |
| contents | In this work, we propose a novel pipeline for face recognition and out-of-distribution (OOD) detection using short-range FMCW radar. The proposed system utilizes Range-Doppler and micro Range-Doppler Images. The architecture features a primary path (PP) responsible for the classification of in-distribution (ID) faces, complemented by intermediate paths (IPs) dedicated to OOD detection. The network is trained in two stages: first, the PP is trained using triplet loss to optimize ID face classification. In the second stage, the PP is frozen, and the IPs-comprising simple linear autoencoder networks-are trained specifically for OOD detection. Using our dataset generated with a 60 GHz FMCW radar, our method achieves an ID classification accuracy of 99.30% and an OOD detection AUROC of 96.91%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08440 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection Kahya, Sabri Mustafa Sivrikaya, Boran Hamdi Yavuz, Muhammet Sami Steinbach, Eckehard Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Signal Processing In this work, we propose a novel pipeline for face recognition and out-of-distribution (OOD) detection using short-range FMCW radar. The proposed system utilizes Range-Doppler and micro Range-Doppler Images. The architecture features a primary path (PP) responsible for the classification of in-distribution (ID) faces, complemented by intermediate paths (IPs) dedicated to OOD detection. The network is trained in two stages: first, the PP is trained using triplet loss to optimize ID face classification. In the second stage, the PP is frozen, and the IPs-comprising simple linear autoencoder networks-are trained specifically for OOD detection. Using our dataset generated with a 60 GHz FMCW radar, our method achieves an ID classification accuracy of 99.30% and an OOD detection AUROC of 96.91%. |
| title | FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2501.08440 |