Multimodal Face Recognition: Combination of Geometry with Physiological Information

Kakadiaris, Ioannis A.; Passalis, G.; Theoharis, T.; Toderici, George; Konstantinidis, I.; Murtuza, N.

doi:10.1109/cvpr.2005.241

Cited by 40 publications

(44 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our deformable model approach achieved a 99 percent rank-one recognition rate for the same database [8].…”

Section: Related Workmentioning

confidence: 95%

“…The proposed integrated system is based on our previous work in face recognition [8], [16], [17], but new additions to our approach (e.g., normal maps and composite alignment algorithm) along with improvements on the existing methods, result in a significant performance gain. Additionally, multiple-sensor databases are used to the best of our knowledge for the first time to evaluate the performance of such a system.…”

Section: Overviewmentioning

confidence: 99%

“…Our approach utilizes an annotated model of the human face (AFM), which needs to be constructed only once and is described in detail in our previous work [8], [16]. This model is subsequently used in alignment and it is deformed in the fitting stage and is the source of the metadata.…”

Section: Annotated Face Modelmentioning

confidence: 99%

See 2 more Smart Citations

Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

Kakadiaris

Passalis

Toderici

et al. 2007

IEEE Trans. Pattern Anal. Mach. Intell.

416

260

View full text Add to dashboard Cite

Abstract-In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality.Index Terms-Face and gesture recognition, information search and retrieval.

show abstract

“…Our deformable model approach achieved a 99 percent rank-one recognition rate for the same database [8].…”

Section: Related Workmentioning

confidence: 95%

Section: Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

Kakadiaris

Passalis

Toderici

et al. 2007

IEEE Trans. Pattern Anal. Mach. Intell.

416

260

View full text Add to dashboard Cite

show abstract

“…Kakadiaris et. al [48] use an anthropomorphically correct AFM and optimize all steps very carefully and obtain very good performance.…”

Section: Automatic Registrationmentioning

confidence: 99%

3D Face Recognition: Technology and Applications

Gökberk

Salah

Alyüz

et al. 2009

Advances in Pattern Recognition

View full text Add to dashboard Cite

“…A choice of the biometrics method can depend upon an application, hardware, memory resources, time constraints and other factors. Modern approaches based on deep learning [17], sparse representation [46] or multimodal biometrics [20] led to good results. Classic methods such as principal component analysis (PCA [43]), linear discriminant analysis (LDA [6]), local descriptors [1] and others are still being enhanced.…”

Section: Introductionmentioning

confidence: 99%

Generalized Choquet Integral for Face Recognition

Karczmarek

Kiersztyn

Pedrycz

2017

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

In this study, we introduce a recent multicriteria decision theory concept of a new, generalized form of Choquet integral function and its application, in particular to the problem of face classification based on the aggregation of classifiers. Such function may be constructed by a simple replacement of the product used under the Choquet integral sign by any t-norm. This idea brings forward a broad class of aggregation operators, which can be incorporated into the decision-making theory. In this context, in a series of experiments we compare the most known t-norms and thoroughly examine their performance in the process of combining individual classifiers based either on facial regions or classic face recognition methods. Such kind of generalization can successfully improve the classification process provided that the parameters of the t-norms are carefully adjusted.

show abstract

Multimodal Face Recognition: Combination of Geometry with Physiological Information

Cited by 40 publications

References 30 publications

Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

3D Face Recognition: Technology and Applications

Generalized Choquet Integral for Face Recognition

Contact Info

Product

Resources

About