3D Assisted Face Recognition: A Survey of 3D Imaging, Modelling and Recognition Approachest

Kittler, Josef; Hilton, Adrian; Hamouz, M.; Illingworth, J.

doi:10.1109/cvpr.2005.377

Cited by 61 publications

(37 citation statements)

References 35 publications

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“…3D face data are less sensitive to lighting variation and pose change, which have been adopted either as an addition or a substitution to face images in recognition tasks [13]. However, 3D faces have their own difficulties.…”

Section: Introductionmentioning

confidence: 99%

Expression-insensitive 3D face recognition using sparse representation

Jia

Zhang

2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

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Section: Introductionmentioning

confidence: 99%

Expression-insensitive 3D face recognition using sparse representation

Jia

Zhang

2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

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“…The main advantage of 3D-based models is that 3D shape does not change under different viewpoints [6]. 3D morphable model (3DMM) [1] is also a statistical model for face reconstruction, and is represented by its shape and texture: the shape is captured as vertices in three dimensions and the texture of the face is conveyed by the colour information of the polygonal patches created by the triplets of neighbouring vertices.…”

Section: Introductionmentioning

confidence: 99%

Resolution-Aware 3D Morphable Model

Hu¹,

Chan²,

Kittler³

et al. 2012

Procedings of the British Machine Vision Conference 2012

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The 3D Morphable Model (3DMM) is currently receiving considerable attention for human face analysis. Most existing work focuses on fitting a 3DMM to high resolution images. However, in many applications, fitting a 3DMM to low-resolution images is also important. In this paper, we propose a Resolution-Aware 3DMM (RA-3DMM), which consists of 3 different resolution 3DMMs: High-Resolution 3DMM (HR-3DMM), Medium-Resolution 3DMM (MR-3DMM) and Low-Resolution 3DMM (LR-3DMM). RA-3DMM can automatically select the best model to fit the input images of different resolutions. The multi-resolution model was evaluated in experiments conducted on PIE and XM2VTS databases. The experimental results verified that HR-3DMM achieves the best performance for input image of high resolution, and MR-3DMM and LR-3DMM worked best for medium and low resolution input images, respectively. A model selection strategy incorporated in the RA-3DMM is proposed based on these results. The RA-3DMM model has been applied to pose correction of face images ranging from high to low resolution. The face verification results obtained with the pose-corrected images show considerable performance improvement over the result without pose correction in all resolutions.

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“…The approach constructs multiple patterns to improve the performance, but may fail in some shots with insufficient exemplars, which is often the case in movies and TV series. The multi-view 3D face model is described in [3] to enhance the video-based face recognition performance. However, it is very difficult to accurately recover the head pose parameters by the state-of-art registration techniques, and therefore not practical for real-world applications.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Cast Indexing for Feature-Length Films

Wang

Wang²,

Bouguet³

et al. 2006

Lecture Notes in Computer Science

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Abstract. Cast indexing is a very important application for contentbased video browsing and retrieval, since the characters in feature-length films and TV series are always the major focus of interest to the audience. By cast indexing, we can discover the main cast list from long videos and further retrieve the characters of interest and their relevant shots for efficient browsing. This paper proposes a novel cast indexing approach based on hierarchical clustering, semi-supervised learning and linear discriminant analysis of the facial images appearing in the video sequence. The method first extracts local SIFT features from detected frontal faces of each shot, and then utilizes hierarchical clustering and Relevant Component Analysis (RCA) to discover main cast. Furthermore, according to the user's feedback, we project all the face images to a set of the most discriminant axes learned by Linear Discriminant Analysis (LDA) to facilitate the retrieval of relevant shots of specified person. Extensive experimental results on movie and TV series demonstrate that the proposed approach can efficiently discover the main characters in such videos and retrieve their associated shots.

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3D Assisted Face Recognition: A Survey of 3D Imaging, Modelling and Recognition Approachest

Cited by 61 publications

References 35 publications

Expression-insensitive 3D face recognition using sparse representation

Expression-insensitive 3D face recognition using sparse representation

Resolution-Aware 3D Morphable Model

Semi-supervised Cast Indexing for Feature-Length Films

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