We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to...We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.展开更多
The 3D face recognition attracts more and more attention because of its insensitivity to the variance of illumination and pose.There are many crucial problems to be solved in this topic,such as 3D face representation ...The 3D face recognition attracts more and more attention because of its insensitivity to the variance of illumination and pose.There are many crucial problems to be solved in this topic,such as 3D face representation and effective multi-feature fusion.In this paper,a novel 3D face recognition algorithm is proposed and its performance is demonstrated on BJUT-3D face database.This algorithm chooses face surface property and the principle component of relative relation matrix as the face representation features.The similarity metric measure for each feature is defined.A feature fusion strategy is proposed.It is a linear weighted strategy based on Fisher linear discriminant analysis.Finally,the presented algorithm is tested on the BJUT-3D face database.It is concluded that the performance of the algorithm and fusion strategy is satisfying.展开更多
In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techni...In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.展开更多
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 mes...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.展开更多
A 3D face recognition approach which uses principal axes registration(PAR)and three face representation features from the re-sampling depth image:Eigenfaces,Fisherfaces and Zernike moments is presented.The approach ad...A 3D face recognition approach which uses principal axes registration(PAR)and three face representation features from the re-sampling depth image:Eigenfaces,Fisherfaces and Zernike moments is presented.The approach addresses the issue of 3D face registration instantly achieved by PAR.Because each facial feature has its own advantages,limitations and scope of use,different features will complement each other.Thus the fusing features can learn more expressive characterizations than a single feature.The support vector machine(SVM)is applied for classification.In this method,based on the complementarity between different features,weighted decision-level fusion makes the recognition system have certain fault tolerance.Experimental results show that the proposed approach achieves superior performance with the rank-1 recognition rate of 98.36%for GavabDB database.展开更多
基金financial supports from the National Natural Science Foundation of China(Grant No.6227511362405124).
文摘We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.
基金Supported by National Natural Science Foundation of China(60533030)Beijing Natural Science Foundation(4061001)
文摘The 3D face recognition attracts more and more attention because of its insensitivity to the variance of illumination and pose.There are many crucial problems to be solved in this topic,such as 3D face representation and effective multi-feature fusion.In this paper,a novel 3D face recognition algorithm is proposed and its performance is demonstrated on BJUT-3D face database.This algorithm chooses face surface property and the principle component of relative relation matrix as the face representation features.The similarity metric measure for each feature is defined.A feature fusion strategy is proposed.It is a linear weighted strategy based on Fisher linear discriminant analysis.Finally,the presented algorithm is tested on the BJUT-3D face database.It is concluded that the performance of the algorithm and fusion strategy is satisfying.
文摘In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.
基金Project(XDA06020300)supported by the"Strategic Priority Research Program"of the Chinese Academy of SciencesProject(12511501700)supported by the Research on the Key Technology of Internet of Things for Urban Community Safety Based on Video Sensor networks
文摘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.
基金The authors would like to acknowledge the use of the GavabDB face database in this paper due to Moreno and Sanchez.This work was supported in part by the National Natural Science Foundation of China(Grant No.60872145)the National High Technology Research and Development Program of China(No.2009AA01Z315)the Cultivation Fund of the Key Scientific and Technical Innovation Project,Ministry of Education of China(No.708085).
文摘A 3D face recognition approach which uses principal axes registration(PAR)and three face representation features from the re-sampling depth image:Eigenfaces,Fisherfaces and Zernike moments is presented.The approach addresses the issue of 3D face registration instantly achieved by PAR.Because each facial feature has its own advantages,limitations and scope of use,different features will complement each other.Thus the fusing features can learn more expressive characterizations than a single feature.The support vector machine(SVM)is applied for classification.In this method,based on the complementarity between different features,weighted decision-level fusion makes the recognition system have certain fault tolerance.Experimental results show that the proposed approach achieves superior performance with the rank-1 recognition rate of 98.36%for GavabDB database.
