Signal‐level fusion for indexing and retrieval of facial biometric data
Abstract The growing scope, scale, and number of biometric deployments around the world emphasise the need for research into technologies facilitating efficient and reliable biometric identification queries. This work presents a method of indexing biometric databases, which relies on signal‐level fu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-03-01
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| Series: | IET Biometrics |
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| Online Access: | https://doi.org/10.1049/bme2.12063 |
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| _version_ | 1850176605843881984 |
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| author | Pawel Drozdowski Fabian Stockhardt Christian Rathgeb Christoph Busch |
| author_facet | Pawel Drozdowski Fabian Stockhardt Christian Rathgeb Christoph Busch |
| author_sort | Pawel Drozdowski |
| collection | DOAJ |
| description | Abstract The growing scope, scale, and number of biometric deployments around the world emphasise the need for research into technologies facilitating efficient and reliable biometric identification queries. This work presents a method of indexing biometric databases, which relies on signal‐level fusion of facial images (morphing) to create a multi‐stage data structure and retrieval protocol. By successively pre‐filtering the list of potential candidate identities, the proposed method makes it possible to reduce the necessary number of biometric template comparisons to complete a biometric identification transaction. The proposed method is extensively evaluated on publicly available databases using open‐source and commercial off‐the‐shelf recognition systems. The results show that using the proposed method, the computational workload can be reduced down to around 30% while the biometric performance of a baseline exhaustive search‐based retrieval is fully maintained, both in closed‐set and open‐set identification scenarios. |
| format | Article |
| id | doaj-art-c19817f85ebb45f0bc0db03ff1eb3741 |
| institution | OA Journals |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-c19817f85ebb45f0bc0db03ff1eb37412025-08-20T02:19:12ZengWileyIET Biometrics2047-49382047-49462022-03-0111214115610.1049/bme2.12063Signal‐level fusion for indexing and retrieval of facial biometric dataPawel Drozdowski0Fabian Stockhardt1Christian RathgebChristoph Busch2da/sec – Biometrics and Internet Security Research Group, Hochschule Darmstadt Darmstadt Germanyda/sec – Biometrics and Internet Security Research Group, Hochschule Darmstadt Darmstadt Germanyda/sec – Biometrics and Internet Security Research Group, Hochschule Darmstadt Darmstadt GermanyAbstract The growing scope, scale, and number of biometric deployments around the world emphasise the need for research into technologies facilitating efficient and reliable biometric identification queries. This work presents a method of indexing biometric databases, which relies on signal‐level fusion of facial images (morphing) to create a multi‐stage data structure and retrieval protocol. By successively pre‐filtering the list of potential candidate identities, the proposed method makes it possible to reduce the necessary number of biometric template comparisons to complete a biometric identification transaction. The proposed method is extensively evaluated on publicly available databases using open‐source and commercial off‐the‐shelf recognition systems. The results show that using the proposed method, the computational workload can be reduced down to around 30% while the biometric performance of a baseline exhaustive search‐based retrieval is fully maintained, both in closed‐set and open‐set identification scenarios.https://doi.org/10.1049/bme2.12063biometric identificationcomputational workload reductionface recognitionindexinginformation fusionmorphing |
| spellingShingle | Pawel Drozdowski Fabian Stockhardt Christian Rathgeb Christoph Busch Signal‐level fusion for indexing and retrieval of facial biometric data IET Biometrics biometric identification computational workload reduction face recognition indexing information fusion morphing |
| title | Signal‐level fusion for indexing and retrieval of facial biometric data |
| title_full | Signal‐level fusion for indexing and retrieval of facial biometric data |
| title_fullStr | Signal‐level fusion for indexing and retrieval of facial biometric data |
| title_full_unstemmed | Signal‐level fusion for indexing and retrieval of facial biometric data |
| title_short | Signal‐level fusion for indexing and retrieval of facial biometric data |
| title_sort | signal level fusion for indexing and retrieval of facial biometric data |
| topic | biometric identification computational workload reduction face recognition indexing information fusion morphing |
| url | https://doi.org/10.1049/bme2.12063 |
| work_keys_str_mv | AT paweldrozdowski signallevelfusionforindexingandretrievaloffacialbiometricdata AT fabianstockhardt signallevelfusionforindexingandretrievaloffacialbiometricdata AT christianrathgeb signallevelfusionforindexingandretrievaloffacialbiometricdata AT christophbusch signallevelfusionforindexingandretrievaloffacialbiometricdata |