Noise versus Facial Expression on 3D Face Recognition

Queirolo,; Segundo,; Bellon,; Silva, Luciano

doi:10.1109/iciap.2007.4362775

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…The Surface Interpenetration Measure (SIM) [19] is computed during the registration process, to perform a precise matching between two face images. The SIM was recently presented for 3D face recognition and the results showed the potential for robustness of this new measure [17]. This capability was confirmed by comprehensive experiments, which is the focus of this work.…”

Section: Introductionsupporting

confidence: 52%

“…This approach is more discriminative than using the SIM value computed from each region by itself [17]. Also, this score is suitable for both identification and verification systems.…”

Section: Sum Rulementioning

confidence: 97%

“…The main idea is to guide matching of a neutral face with another one with expression to the face most invariant areas (e.g. forehead and nose regions) [17].…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

“…Also, an enhanced SA-based approach was used to handle facial expression effects during face matching [17]. The main idea is to guide matching of a neutral face with another one with expression to the face most invariant areas (e.g.…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

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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: Introductionsupporting

confidence: 52%

“…This approach is more discriminative than using the SIM value computed from each region by itself [17]. Also, this score is suitable for both identification and verification systems.…”

Section: Sum Rulementioning

confidence: 97%

“…The main idea is to guide matching of a neutral face with another one with expression to the face most invariant areas (e.g. forehead and nose regions) [17].…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

See 2 more Smart Citations

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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“…The most popular region is positioned around the nose, as in [55], [59], [62], [65], [68]- [71], [73]- [75], [83], and [81]. Another anatomic region that is used is the eyes/forehead region [59], [62], [70], [78]. The second strategy to determine expression-invariant regions is the use of local features.…”

Section: A Region-based Methodsmentioning

confidence: 99%

A Comparative Study of 3-D Face Recognition Under Expression Variations

Smeets

Claes

Hermans

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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Research in face recognition has continuously been challenged by extrinsic (head pose, lighting conditions) and intrinsic (facial expression, aging) sources of variability. While many survey papers on face recognition exist, in this paper, we focus on a comparative study of 3-D face recognition under expression variations. As a first contribution, 3-D face databases with expressions are listed, and the most important ones are briefly presented and their complexity is quantified using the iterative closest point (ICP) baseline recognition algorithm. This allows to rank the databases according to their inherent difficulty for face-recognition tasks. This analysis reveals that the FRGC v2 database can be considered as the most challenging because of its size, the presence of expressions and outliers, and the time lapse between the recordings. Therefore, we recommend to use this database as a reference database to evaluate (expression-invariant) 3-D face-recognition algorithms. We also determine and quantify the most important factors that influence the performance. It appears that performance decreases 1) with the degree of nonfrontal pose, 2) for certain expression types, 3) with the magnitude of the expressions, 4) with an increasing number of expressions, and 5) for a higher number of gallery subjects. Future 3-D face-recognition algorithms should be evaluated on the basis of all these factors. As the second contribution, a survey of published 3-D face-recognition methods that deal with expression variations is given. These methods are subdivided into three classes depending on the way the expressions are handled. Region-based methods use expression-stable regions only, while other methods model the expressions either using an isometric or a statistical model. Isometric models assume the deformation because of expression variation to be (locally) isometric, meaning that the deformation preserves lengths along the surface. Statistical models learn how the facial soft tissue deforms during expressions based on a training database with expression labels. Algorithmic performances are evaluated by the comparison of recognition rates for identification and verification. No statistical significant differences in class performance are found between any pair of classes.

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