Facial pose from 3D data

Rajwade, Ajit; Levine, Martin D.

doi:10.1016/j.imavis.2006.02.010

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…The weakness of this method is that it requires a large variety of nose models to compare and categorize different types of profiles. A similar method proposed by Rajwade et al [19] for detection and automatic pose correction using Support Vector Regression (SVR) on the sub-band wavelet. This technique is able to accurately classify subjects with head poses of ±9 • in both X and Y-axes.…”

Section: Related Workmentioning

confidence: 99%

3D face verification across pose based on euler rotation and tensors

Chouchane¹,

Ouamane²,

Boutellaa³

et al. 2017

Multimed Tools Appl

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In this paper, we propose a new approach for 3D face verification based on tensor representation. Face challenges, such as illumination, expression and pose, are modeled as a multilinear algebra problem where facial images are represented as high order tensors. Particularly, to account for head pose variations, several pose scans are generated from a single depth image using the Euler transformation. Multi-bloc local phase quantization (MB-LPQ) histogram features are extracted from depth face images and arranged as a third order tensor. The dimensionality of the tensor is reduced based on the higher-order singular value decomposition (HOSVD). HOSVD projects the input tensor in a new subspace in which the dimension of each tensor mode is reduced. To discriminate faces from different persons, we utilize the Enhanced Fisher Model (EFM). Experimental evaluations on CASIA-3D database, which contains large head pose variations, demonstrate the effectiveness of the proposed approach. A verification rate of 98.60% is obtained. Index Terms 3D face verification, tensor analysis, multilinear dimensionality reduction, Euler angles. I. INTRODUCTION In real world applications, face recognition is confronted to several challenges, in terms of, for e.g., illuminations, expressions, occlusions and poses variations. This situation has highly motivated researchers from the fields of computer vision and pattern recognition to consider face recognition challenge under so called 'in the wild' conditions, referring to face images acquired A. Chouchane, A.

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Section: Related Workmentioning

confidence: 99%

3D face verification across pose based on euler rotation and tensors

Chouchane¹,

Ouamane²,

Boutellaa³

et al. 2017

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…For instance, (Tang et al, 2011a;Seemann et al, 2004) employed neural network (NN) to train and test their 3D face pose estimators. (Rajwade and Levine, 2006) utilized support vector regression (SVR) based on 3D data to estimate facial pose. Furthermore, random regression forests (RRF) is utilized in (Fanelli et al, 2011a;Fanelli et al, 2011b;Tang et al, 2011b) to solve depth data based face pose estimation problem.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach for Depth Data based Face Pose Estimation using Particle Swarm Optimization

Mou

Wang

2014

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

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This paper presents an improved approach for face pose estimation based on depth data using particle swarm optimization (PSO). In this approach, the frontal face of the system-user is first initialized and its depth image is taken as a person-specific template. Each query face of that user is rotated and translated with respect to its centroid using PSO to match with the template. Since the centroid of each query face always changes with the face pose changing, a common reference point has to be defined to measure the exact transformation of the query face. Thus, the nose tips of the optimal transformed face and the query face are localized to recompute the transformation from the query face to the optimal transformed face that matched with the template. Using the recomputed rotation and translation information, finally, the pose of the query face can be approximated by the relative pose between the query face and the template face. Experiments on public database show that the accuracy of this new method is more than 99%, which is much higher than the best performance (< 91%) of existing work.

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“…In some cases, the problem is tackled from a tracking point of view that can be solved by fusing multiple cues [31] or using infrared light to detect the pupils [17]. In others, range information is used to improve the results [24,29].…”

Section: Introductionmentioning

confidence: 99%

Multi-camera head pose estimation

Muñoz-Salinas

Yeguas-Bolivar

Saffiotti

et al. 2012

Machine Vision and Applications

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Estimating people's head pose is an important problem, for which many solutions have been proposed. Most existing solutions are based on the use of a single camera and assume that the head is confined in a relatively small region of space. If we need to estimate unintrusively the head pose of persons in a large environment, however, we need to use several cameras to cover the monitored area. In this work, we propose a novel solution to the multi-camera head pose estimation problem that exploits the additional amount of information that provides multi-camera configurations. Our approach uses the probability estimates produced by multiclass support vector machines to calculate the probability distribution of the head pose. The distributions produced by the cameras are fused, resulting in a more precise estimate than the one provided individually. We report experimental results that confirm that the fused distribution provides higher accuracy than the individual classifiers and a high robustness against errors.

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Facial pose from 3D data

Cited by 13 publications

References 26 publications

3D face verification across pose based on euler rotation and tensors

3D face verification across pose based on euler rotation and tensors

An Improved Approach for Depth Data based Face Pose Estimation using Particle Swarm Optimization

Multi-camera head pose estimation

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