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			<subfield code="a">CONF</subfield>
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		<datafield tag="970" ind1=" " ind2=" ">
			<subfield code="a">Bhattacharjee_ICPR_2024/IDIAP</subfield>
		</datafield>
		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Vascular Biometrics Experiments on Candy -- A New Contactless Finger-Vein Dataset</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Bhattacharjee, Sushil</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Geissbuhler, David</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Clivaz, G.</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Kotwal, Ketan</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Marcel, Sébastien</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/papers/2024/Bhattacharjee_ICPR_2024.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="711" ind1="2" ind2=" ">
			<subfield code="a">Proceedings of the International Conference on Pattern Recognition (ICPR)</subfield>
			<subfield code="c">Calcutta (India)</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2024</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">The topic of finger-vein (FV) biometrics is an active and growing topic of research. Most FV systems available today rely on contact sensors that capture vein patterns of a single finger at a time. We have recently completed a project aimed at designing a contactless vein sensing platform, named sweet. In this paper we present a new FV dataset collected using sweet. The dataset includes multiple FV samples from 120 subjects, and 280 presentation attack instruments (PAI), captured in a contactless manner. Further, we present baseline FV authentication (FVA) results achieved for proposed dataset. The sweet platform is equipped to capture a sequence of images suitable for photometric-stereo (PS) reconstruction of 3D surfaces. We present a FV presentation attack detection (PAD) method based on PS reconstruction, and the corresponding baseline FV PAD results on the proposed dataset.</subfield>
		</datafield>
	</record>
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