3D Face Image Registration for Face Matching Guided by the Surface Interpenetration Measure

Bellon, Olga Regina Pereira; Silva, Luciano; Queirolo, Chauã C.; Drovetto, Sídnei A.; Pamplona, Maurucio

doi:10.1109/icip.2006.313057

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Earlier results, presented in [1], have shown that the SA produces more accurate alignments and a higher recognition rate as opposed to ICP. A brief explanation of the SIM and the SA approach for range image registration are presented as follows.…”

Section: Matchingmentioning

confidence: 90%

3D face recognition using the Surface Interpenetration Measure: A comparative evaluation on the FRGC database

Queirolo¹,

Silva²,

Bellon³

et al. 2008

2008 19th International Conference on Pattern Recognition

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This paper focuses a comparative evaluation of our framework for 3D face recognition and state-of-theart systems. Our method uses a Simulated Annealingbased approach (SA) for range image registration with the Surface Interpenetration Measure (SIM) as the similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions. Experiments were performed on the FRGC v2 database simulating both verification and identification systems and the obtained results were compared to those reported in the literature. By using all the images in the database, a verification rate of 95.9% was achieved, at a False Acceptance Rate (FAR) of 0.1%. In the identification scenario, a rank-one accuracy of 99.5% was obtained. To our knowledge, this is the best rank-one score obtained on the FRGC v2 database, as compared to previously published results.

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Section: Matchingmentioning

confidence: 90%

3D face recognition using the Surface Interpenetration Measure: A comparative evaluation on the FRGC database

Queirolo¹,

Silva²,

Bellon³

et al. 2008

2008 19th International Conference on Pattern Recognition

Self Cite

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“…An example of the registration process applied to facial images from the same person is shown in Figure 5. Our initial works aimed to validate the SIM as a robust and reliable measure for face matching [17], [18]. The following works were focused on the development of a fully automatic 3D face recognition system.…”

Section: B 3d Face Recognitionmentioning

confidence: 99%

IMAGO Research Group

Silva

Bellon

Segundo

et al. 2011

JIS

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The IMAGO Research Group was founded in 1996 and accumulated great expertise in range image analysis during its existence. The group has been working on projects related to computer vision, image processing and computer graphics in order to produce solutions for different areas, such as culture, education, social inclusion, health and security. The efforts of the group are currently focused on digital preservation and biometric recognition, in which there are three main ongoing projects: 3D Virtual Museum, 3D Face Recognition and Newborn Identification. These projects have virtual reality and 3D interaction features beyond the HCI requirements, that provides motivation in the use of our systems.

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“…We propose to perform 3D face matching by combining a precise registration technique and the SIM (Bellon et al, 2006) to evaluate different facial regions. The registration is obtained by a SA based approach, which have two important advantages: (1) it tends to find a near global solution;…”

Section: D Face Matchingmentioning

confidence: 99%

Hierarchical Evaluation Model for 3d Face Recognition

2008

Proceedings of the Third International Conference on Computer Vision Theory and Applications

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In this paper we propose to perform 3D face matching based on alignments obtained using Simulated Annealing (SA) algorithm guided by the Mean Squared Error (MSE) with M-estimator Sample Consensus (MSAC) and the Surface Interpenetration Measure (SIM). The matching score is obtained by calculation of the SIM after the registration process. Since the SIM is a sensitive measure, it needs a good alignment to give relevance to its value. Our registration approach tends to reach a near global solution and, therefore, produces the necessary precise alignments. By analyzing the matching score, the system can identify if the input images come from the same subject or not. In a verification scenario, we use a hierarchical evaluation model which maximizes the results and reduces the computing time. Extensive experiments were performed on the wellknown Face Recognition Grand Challenge (FRGC) v2.0 3D face database using five different facial regions: three regions of the nose; the region of the eyes; and the face itself. Compared to state-of-the-art works, our approach has achieved a high rank-one recognition rate and a high verification rate.

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3D Face Image Registration for Face Matching Guided by the Surface Interpenetration Measure

Cited by 8 publications

References 15 publications

3D face recognition using the Surface Interpenetration Measure: A comparative evaluation on the FRGC database

3D face recognition using the Surface Interpenetration Measure: A comparative evaluation on the FRGC database

IMAGO Research Group

Hierarchical Evaluation Model for 3d Face Recognition

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