FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929377081556992 |
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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 | This paper proposes a short-range FMCW radar-based facial authentication and out-of-distribution (OOD) detection framework. Our pipeline jointly estimates the correct classes for the in-distribution (ID) samples and detects the OOD samples to prevent their inaccurate prediction. Our reconstruction-based architecture consists of a main convolutional block with one encoder and multi-decoder configuration, and intermediate linear encoder-decoder parts. Together, these elements form an accurate human face classifier and a robust OOD detector. For our dataset, gathered using a 60 GHz short-range FMCW radar, our network achieves an average classification accuracy of 98.07% in identifying in-distribution human faces. As an OOD detector, it achieves an average Area Under the Receiver Operating Characteristic (AUROC) curve of 98.50% and an average False Positive Rate at 95% True Positive Rate (FPR95) of 6.20%. Also, our extensive experiments show that the proposed approach outperforms previous OOD detectors in terms of common OOD detection metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar Kahya, Sabri Mustafa Sivrikaya, Boran Hamdi Yavuz, Muhammet Sami Steinbach, Eckehard Computer Vision and Pattern Recognition Machine Learning Signal Processing This paper proposes a short-range FMCW radar-based facial authentication and out-of-distribution (OOD) detection framework. Our pipeline jointly estimates the correct classes for the in-distribution (ID) samples and detects the OOD samples to prevent their inaccurate prediction. Our reconstruction-based architecture consists of a main convolutional block with one encoder and multi-decoder configuration, and intermediate linear encoder-decoder parts. Together, these elements form an accurate human face classifier and a robust OOD detector. For our dataset, gathered using a 60 GHz short-range FMCW radar, our network achieves an average classification accuracy of 98.07% in identifying in-distribution human faces. As an OOD detector, it achieves an average Area Under the Receiver Operating Characteristic (AUROC) curve of 98.50% and an average False Positive Rate at 95% True Positive Rate (FPR95) of 6.20%. Also, our extensive experiments show that the proposed approach outperforms previous OOD detectors in terms of common OOD detection metrics. |
| title | FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar |
| topic | Computer Vision and Pattern Recognition Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2406.04546 |