Shallow CNNs for the Reliable Detection of Facial Marks

Chris Zeinstra; Erwin Haasnoot

doi:10.23919/BIOSIG.2018.8553157

Shallow CNNs for the Reliable Detection of Facial Marks

Chris Zeinstra, Erwin Haasnoot

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

2 Downloads (Pure)

Abstract

Facial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers. We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.

Original language	English
Title of host publication	2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018
Editors	Arslan Bromme, Andreas Uhl, Christoph Busch, Christian Rathgeb, Antitza Dantcheva
Place of Publication	Piscataway, NJ
Publisher	IEEE
ISBN (Electronic)	978-3-88579-676-3
ISBN (Print)	978-1-5386-6007-2
DOIs	https://doi.org/10.23919/BIOSIG.2018.8553157
Publication status	Published - 10 Oct 2018
Event	17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018 - Darmstadt, Germany Duration: 26 Sept 2018 → 28 Sept 2018 Conference number: 17

Publication series

Name	International Conference of the Biometrics Special Interest Group (BIOSIG)
Publisher	IEEE
Volume	2018
ISSN (Electronic)	1617-5468

Conference

Conference	17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018
Abbreviated title	BIOSIG 2018
Country/Territory	Germany
City	Darmstadt
Period	26/09/18 → 28/09/18

Keywords

CNN
Facial marks
Forensics
Image processing

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10.23919/BIOSIG.2018.8553157

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Zeinstra, C., & Haasnoot, E. (2018). Shallow CNNs for the Reliable Detection of Facial Marks. In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 Article 8553157 (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553157

Zeinstra, Chris ; Haasnoot, Erwin. / Shallow CNNs for the Reliable Detection of Facial Marks. 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018. editor / Arslan Bromme ; Andreas Uhl ; Christoph Busch ; Christian Rathgeb ; Antitza Dantcheva. Piscataway, NJ : IEEE, 2018. (International Conference of the Biometrics Special Interest Group (BIOSIG)).

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title = "Shallow CNNs for the Reliable Detection of Facial Marks",

abstract = "Facial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers. We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.",

keywords = "CNN, Facial marks, Forensics, Image processing",

author = "Chris Zeinstra and Erwin Haasnoot",

year = "2018",

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booktitle = "2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018",

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Zeinstra, C & Haasnoot, E 2018, Shallow CNNs for the Reliable Detection of Facial Marks. in A Bromme, A Uhl, C Busch, C Rathgeb & A Dantcheva (eds), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018., 8553157, International Conference of the Biometrics Special Interest Group (BIOSIG), vol. 2018, IEEE, Piscataway, NJ, 17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018, Darmstadt, Hesse, Germany, 26/09/18. https://doi.org/10.23919/BIOSIG.2018.8553157

Shallow CNNs for the Reliable Detection of Facial Marks. / Zeinstra, Chris ; Haasnoot, Erwin.
2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018. ed. / Arslan Bromme; Andreas Uhl; Christoph Busch; Christian Rathgeb; Antitza Dantcheva. Piscataway, NJ: IEEE, 2018. 8553157 (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Zeinstra, Chris

AU - Haasnoot, Erwin

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PY - 2018/10/10

Y1 - 2018/10/10

N2 - Facial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers. We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.

AB - Facial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers. We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.

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T3 - International Conference of the Biometrics Special Interest Group (BIOSIG)

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A2 - Busch, Christoph

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Zeinstra C , Haasnoot E. Shallow CNNs for the Reliable Detection of Facial Marks. In Bromme A, Uhl A, Busch C, Rathgeb C, Dantcheva A, editors, 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018. Piscataway, NJ: IEEE. 2018. 8553157. (International Conference of the Biometrics Special Interest Group (BIOSIG)). doi: 10.23919/BIOSIG.2018.8553157

Shallow CNNs for the Reliable Detection of Facial Marks

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