2D&amp;#x2013;3D face matching using CCA

Yang, Wei; Dong, Yi; Lei, Zhen; Sang, Jitao; Li, S.Z.

doi:10.1109/afgr.2008.4813407

Cited by 45 publications

(30 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Example synthesized results obtained from the proposed framework are shown in Fig 5 and Figure 7 shows the synthesis results obtained using various existing kernel methods, namely the Eigen-transformation method in [6], the one-dimensional canonical correlation analysis (1DCCA) method in [10], the two-dimensional CCA (2DCCA) method in [3], the direct combined model (1DDCM) in [7], and the two-dimensional DCM method (2DDCM) in [8]. It is observed that the synthesized results obtained using the 2DDCM approach and 2DCCA approach are qualitatively better than the others.…”

Section: Experiments Resultsmentioning

confidence: 99%

Face Sketch Synthesis System Utilizing Both Global Structure and Local Detailed Texture Components

Chan

2016

Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016

View full text Add to dashboard Cite

This study developed a facial sketch synthesis system based on the two-dimensional direct combined model (2DDCM) approach that employs a collection of photo/sketch pairwise training samples. The proposed synthesis framework addresses the following key issues. First, we directly combine each photo and sketch pairwise sample in a concatenated form in order to completely preserve their relationship. Second, photo and sketch images are formed as two-dimensional matrices instead of vectors in order to preserve the facial geometry. Third, both the global facial geometry and the local detailed textures are included in the proposed synthesis framework. Experiments demonstrate our approach can synthesize high quality reconstructed facial sketches from given unseen photos.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Face Sketch Synthesis System Utilizing Both Global Structure and Local Detailed Texture Components

Chan

2016

Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016

View full text Add to dashboard Cite

show abstract

“…In general, the main drawback with this approach is the time spent in labeling and learning the model. [Yang et al, 2008] use Canonical Correlation Analysis (CCA) for face recognition to learn a correlation between image features and 3D features of face data. In this application, an image and a 3D mesh of a face are compared: if there is sufficient correlation then it is deemed a match.…”

Section: Related Workmentioning

confidence: 99%

A Saliency-based Framework for 2D-3D Registration

Brown

Guillemaut

Windridge

2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

View full text Add to dashboard Cite

Abstract:Here we propose a saliency-based filtering approach to the problem of registering an untextured 3D object to a single monocular image. The principle of saliency can be applied to a range of modalities and domains to find intrinsically descriptive entities from amongst detected entities, making it a rigorous approach to multi-modal registration. We build on the Kadir-Brady saliency framework due to its principled information-theoretic approach which enables us to naturally extend it to the 3D domain. The salient points from each domain are initially aligned using the SoftPosit algorithm. This is subsequently refined by aligning the silhouette with contours extracted from the image. Whereas other point based registration algorithms focus on corners or straight lines, our saliency-based approach is more general as it is more widely applicable e.g. to curved surfaces where a corner detector would fail. We compare our salient point detector to the Harris corner and SIFT keypoint detectors and show it generally achieves superior registration accuracy.

show abstract

“…Their approach splits the face recognition task into two steps: (1) a matching step respectively processed in 2D/2D; (2) 3D/2D a fusion step combining two matching scores. Canonical correlation analysis (CCA) is applied in method proposed by Yang et al [14]. They apply CCA to learn the mapping between the 2D face image and 3D face data.…”

Section: Introductionmentioning

confidence: 99%

The use of hidden Markov models to verify the identity based on facial asymmetry

Kubanek

Bobulski

2017

J Image Video Proc.

View full text Add to dashboard Cite

This work concerns the use of biometric features, resulting from the look of a face, for the verification purposes. Different methods of selection and feature analysis during face recognition are presented here. The description contains mainly analysis possibilities and also identity verification based on asymmetric facial features-in later stages. The new verification method has been introduced based on designated characteristic points. These points were designated through appropriate coded information about facial asymmetry as observation vectors and recognition using hidden Markov models. The advantage of these models is that we can use vectors of different lengths. Such vectors appear in incomplete data when all points cannot be located.

show abstract

2D–3D face matching using CCA

Cited by 45 publications

References 10 publications

Face Sketch Synthesis System Utilizing Both Global Structure and Local Detailed Texture Components

Face Sketch Synthesis System Utilizing Both Global Structure and Local Detailed Texture Components

A Saliency-based Framework for 2D-3D Registration

The use of hidden Markov models to verify the identity based on facial asymmetry

Contact Info

Product

Resources

About