An Efficient 3D Face Recognition Algorithm

Kaushik, Vandana Dixit; Budhwar, Aditya; Dubey, Anuj; Agrawal, Rahul; Gupta, Soujit Sen; Pathak, Vinay Kumar; Gupta, Phalguni

doi:10.1109/ntms.2009.5384750

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…In addition, with approximately the same number of probe images, we achieved a higher rate compared to the Mpiperis et al [15]. Kaushik et al [16] reported above 98% recognition rate which seems is better than our result but they have used a limited number of probes. Furthermore, our result is similar to Lie et al [7].…”

Section: Recognition Resultscontrasting

confidence: 55%

Multimodal expression-invariant face recognition using dual-tree complex wavelet transform

Ayatollahi

Raie

Hajati

2013

2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)

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A new multimodal face recognition method which extracts features of rigid and semi-rigid regions of the face using Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed. DT-CWT decomposes range and intensity images into eight subimages consisting of six band-pass sub-images to represent face details and two low-pass sub-images to represent face approximates. In this work, support vector machine (SVM) has been used as the classifier. The proposed method has been evaluated using the face BU-3DFE dataset containing a wide range of expression changes. Findings include the overall identification rate of 98.1% and the overall verification rate of 99.3% at 0.1% false acceptance rate.

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Section: Recognition Resultscontrasting

confidence: 55%

Multimodal expression-invariant face recognition using dual-tree complex wavelet transform

Ayatollahi

Raie

Hajati

2013

2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)

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“…In statistical models that are derived from 3-D shapes, ICP often provides correspondence information, as in [90] (combined with features for initialization), [91] (on the rigid parts of the face) and [92] (with extra energy terms for regularization and larger convergence area). In [93], anatomic landmarks are detected as correspondence information.…”

Section: B Statistical Modeling Methodsmentioning

confidence: 99%

“…However, model fitting becomes computationally more demanding. Other statistical models use independent component analysis (ICA) [44], linear discriminant analysis (LDA) [88] or simply pointwise mean and standard deviation [93].…”

Section: B Statistical Modeling Methodsmentioning

confidence: 99%

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A Comparative Study of 3-D Face Recognition Under Expression Variations

Smeets

Claes

Hermans

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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Research in face recognition has continuously been challenged by extrinsic (head pose, lighting conditions) and intrinsic (facial expression, aging) sources of variability. While many survey papers on face recognition exist, in this paper, we focus on a comparative study of 3-D face recognition under expression variations. As a first contribution, 3-D face databases with expressions are listed, and the most important ones are briefly presented and their complexity is quantified using the iterative closest point (ICP) baseline recognition algorithm. This allows to rank the databases according to their inherent difficulty for face-recognition tasks. This analysis reveals that the FRGC v2 database can be considered as the most challenging because of its size, the presence of expressions and outliers, and the time lapse between the recordings. Therefore, we recommend to use this database as a reference database to evaluate (expression-invariant) 3-D face-recognition algorithms. We also determine and quantify the most important factors that influence the performance. It appears that performance decreases 1) with the degree of nonfrontal pose, 2) for certain expression types, 3) with the magnitude of the expressions, 4) with an increasing number of expressions, and 5) for a higher number of gallery subjects. Future 3-D face-recognition algorithms should be evaluated on the basis of all these factors. As the second contribution, a survey of published 3-D face-recognition methods that deal with expression variations is given. These methods are subdivided into three classes depending on the way the expressions are handled. Region-based methods use expression-stable regions only, while other methods model the expressions either using an isometric or a statistical model. Isometric models assume the deformation because of expression variation to be (locally) isometric, meaning that the deformation preserves lengths along the surface. Statistical models learn how the facial soft tissue deforms during expressions based on a training database with expression labels. Algorithmic performances are evaluated by the comparison of recognition rates for identification and verification. No statistical significant differences in class performance are found between any pair of classes.

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“…Although the performance of algorithm in Ref. [49] is slightly superior to that of the proposed approach, the average number of each subject in the gallery set in their experiments is 6.95, which is more than one.…”

Section: Experiments On Bu-3dfe Datasetmentioning

confidence: 90%

Face recognition using SIFT features under 3D meshes

Cheng

et al. 2015

J. Cent. South Univ.

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Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform (SIFT) features on 3D meshes. After preprocessing, shape index extrema on the 3D facial surface are selected as keypoints in the difference scale space and the unstable keypoints are removed after two screening steps. Then, a local coordinate system for each keypoint is established by principal component analysis (PCA). Next, two local geometric features are extracted around each keypoint through the local coordinate system. Additionally, the features are augmented by the symmetrization according to the approximate left-right symmetry in human face. The proposed method is evaluated on the Bosphorus, BU-3DFE, and Gavab databases, respectively. Good results are achieved on these three datasets. As a result, the proposed method proves robust to facial expression variations, partial external occlusions and large pose changes.

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An Efficient 3D Face Recognition Algorithm

Cited by 5 publications

References 10 publications

Multimodal expression-invariant face recognition using dual-tree complex wavelet transform

Multimodal expression-invariant face recognition using dual-tree complex wavelet transform

A Comparative Study of 3-D Face Recognition Under Expression Variations

Face recognition using SIFT features under 3D meshes

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