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			<subfield code="a">George_IJCB2023-2_2023/IDIAP</subfield>
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			<subfield code="a">EFaR 2023: Efficient Face Recognition Competition</subfield>
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			<subfield code="a">George, Anjith</subfield>
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			<subfield code="a">Ecabert, Christophe</subfield>
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			<subfield code="a">Otroshi Shahreza, Hatef</subfield>
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			<subfield code="a">Kotwal, Ketan</subfield>
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			<subfield code="a">Marcel, Sébastien</subfield>
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			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/papers/2023/George_IJCB2023-2_2023.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
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			<subfield code="a">IEEE International Joint Conference on Biometrics (IJCB 2023)</subfield>
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			<subfield code="c">2023</subfield>
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			<subfield code="x">2474-9680</subfield>
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			<subfield code="a">This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held within the 2023 In- ternational Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recog- nition models, the submitted solutions are ranked based on a weighted score of the achieved verification accura- cies on a diverse set of benchmarks, as well as the de- ployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition bench- marks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines, the methodologies used to achieve lightweight and efficient face recognition solutions, and out- looks on possible techniques that are underrepresented in current solutions.</subfield>
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