New Experiments on ICP-Based 3D Face Recognition and Authentication

Amor, Boulbaba Ben; Ardabilian, Mohsen; Chen, L.

doi:10.1109/icpr.2006.855

Cited by 37 publications

(20 citation statements)

References 11 publications

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“…As for the 3D, since both databases do not include any facial expressions, Iterative Closest Point (ICP) method is selected to register surface pairs to each other and ICP error is simply taken as a measure of how well they match. Many early studies for 3D face recognition propose this convention [29], [30], [31]. These algorithms are selected simply to expand the number of different face recognition methods whose vulnerabilities are analysed against 3D mask attacks.…”

Section: Baseline Face Recognition Algorithmsmentioning

confidence: 99%

Spoofing Face Recognition With 3D Masks

Erdoğmuş

Marcel

2014

IEEE Trans.Inform.Forensic Secur.

263

151

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Abstract-Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In the literature, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them.Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type.In order to assess the spoofing performance of 3D masks against 2D, 2.5D and 3D face recognition and to analyse various texture based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two datasets: The Morpho database which is not publicly available and the newly distributed 3D Mask Attack Database (3DMAD).

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Section: Baseline Face Recognition Algorithmsmentioning

confidence: 99%

Spoofing Face Recognition With 3D Masks

Erdoğmuş

Marcel

2014

IEEE Trans.Inform.Forensic Secur.

263

151

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“…We have concluded that the less the deformation is, the invariant the ICP-based approach is [2]. In the present approach, we suggest to associate different weights to different areas of the face.…”

Section: D Facial Split and Segmentationmentioning

confidence: 99%

“…Our first experiments based on global-ICP [2], without considering the region-based similarity metric, show that the defect of the rigid matching process is linked to its sensitivity to 3D shape deformation. We have concluded that the less the deformation is, the invariant the ICP-based approach is [2].…”

Section: D Facial Split and Segmentationmentioning

confidence: 99%

Enhancing 3D Face Recognition By Mimics Segmentation

Amor

Ardabilian

Chen

2006

Sixth International Conference on Intelligent Systems Design and Applications

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“…Therefore, the ICP error, which serves as a measure of how well the surfaces match serves as the feature extraction and matching technique. Many early systems for 3DFR use this convention [19], [60], [8]. Some representation techniques such as the point signatures [23,95], spin images [96], or histogram based approaches [100] are special in that they do not require prior registration.…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%