3D Object Representation Using Transform and Scale Invariant 3D Features

Abstract.A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented in this paper. This database is unique from three aspects: i) the facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; ii) a rich set of head pose variations are available; and iii) different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.

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“…A recently introduced generic transformation invariant 3D feature detector [15] is being experimented to locate scanned faces in 3D space.…”

Section: Discussionmentioning

confidence: 99%

Bosphorus Database for 3D Face Analysis

Savran

Alyüz

Dibeklioğlu

et al. 2008

Lecture Notes in Computer Science

663

391

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“…meshSIFT which is developed by [3] is widely used in 3D salient points identification and face recognition. [13], inspired by the scaleinvariant feature transform (SIFT), described the facial surface with the mean and Gaussian curvatures to identify the facial components (chin, nose and eye pits). In [14], a 3D keypoint detector and descriptor based on (SIFT) [8], has been designed and used to perform 3D face recognition.…”

Section: A Related Workmentioning

confidence: 99%

Landmarks detection on 3D face scans using local histogram descriptors

Tortorici

Hassan

et al. 2016

2016 18th Mediterranean Electrotechnical Conference (MELECON)

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In this work, we exploit 3D Constrained Local Model (CLM) for facial landmark detection. Our approach integrates the geometric information of 3D face scans. The fast increase demand of 3D data invite to develop 3D image processing methods for many applications and especially for automatic landmark detection. The new step in this paper is the introduction of mesh histogram of gradients (meshHOG) as local descriptors around every landmark location. The proposed work is evaluated on the publicly available Bosphorus database. A comparison with the other descriptors mesh LBP and mesh SIFT are also depicted.

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“…Canonical face depth maps [13] create a smaller representation for 3D face data, while work like symbolic surface curvatures [14] concentrated on exactly describing a specific local facial feature. There is also a significant body of work on 3D landmarks and features ranging from landmark detection to appropriate analysis of facial features [15][16][17][18]. In each of these cases, landmarks are either hand-labeled or induced from previously labeled faces.…”

Section: Introductionmentioning

confidence: 99%

Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research

Wilamowska

Heike

et al. 2011

J Digit Imaging

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3D imaging systems are used to construct high-resolution meshes of patient's heads that can be analyzed by computer algorithms. Our work starts with such 3D head meshes and produces both global and local descriptors of 3D shape. Since these descriptors are numeric feature vectors, they can be used in both classification and quantification of various different abnormalities. In this paper, we define these descriptors, describe our methodology for constructing them from 3D head meshes, and show through a set of classification experiments involving cases and controls for a genetic disorder called 22q11.2 deletion syndrome that they are suitable for use in craniofacial research studies. The main contributions of this work include: automatic generation of novel global and local data representations, robust automatic placement of anthropometric landmarks, generation of local descriptors for nasal and oral facial features from landmarks, use of local descriptors for predicting various local facial features, and use of global features for 22q11.2DS classification, showing their potential use as descriptors in craniofacial research.

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3D Object Representation Using Transform and Scale Invariant 3D Features

Cited by 25 publications

References 25 publications

Bosphorus Database for 3D Face Analysis

Bosphorus Database for 3D Face Analysis

Landmarks detection on 3D face scans using local histogram descriptors

Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research

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