68 Landmarks are Efficient for 3D Face Alignment: What about More? 3D Face Alignment Method Applied to Face Recognition

Jabberi, Marwa; Wali, Ali; Chaudhuri, B. B.; Alimi, Adel M.

doi:10.36227/techrxiv.16742077.v2

Cited by 2 publications

(5 citation statements)

References 54 publications

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“…It is worth mentioning that this stage is time-consuming; however, as it was a part of our previous work, we opted for using it [52]. Furthermore, we thought of the good quality of the mesh built once the initial one was obtained in case of using 2D datasets.…”

Section: D Face Recognition Based On 3d Shapenets a Overall Pipelinementioning

confidence: 99%

“…So, we establish 3D face reconstruction from faces in 2D datasets since our objective is to extract features using deep 3D CNN which is 3D ShapeNets and the input should be a 3D volume applied to perform 3D face recognition. In a previous work [52], we proposed 3D face reconstruction in the context of face alignment and frontalization. This implies that 3D reconstruction may be used to deal with several 2D FR problems and limitations.…”

Section: B Data Preprocessing 1) 3d Face Reconstructionmentioning

confidence: 99%

“…This implies that 3D reconstruction may be used to deal with several 2D FR problems and limitations. In Pseudo Code (1), we present the generalized steps of 3D reconstruction (details are presented in our previous work [52]). 2) Facial Surface Extraction At this stage, each 3D face follows some pretreatment steps.…”

Section: B Data Preprocessing 1) 3d Face Reconstructionmentioning

confidence: 99%

“…So, correction consisting in missing parts reconstruction and meshing is made. This correction is detailed in our previous research work [52].…”

Section: Figure 5 Facial Surface Extraction Using Region Growing Segm...mentioning

confidence: 99%

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Face ShapeNets for 3D Face Recognition

et al. 2023

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In this paper, we present a deep learning-based method for 3D face recognition. Unlike some previous works, our process does not rely on face representation methods as a proxy step to be accepted by Convolutional Neural Networks (CNNs). Applying 2D CNNs to irregular domains such as 3D meshes is challenging. Therefore, we employed 3D ShapeNets to recognize faces covering the full 3D shape since 3D face datasets are available and 3D data augmentation techniques to enlarge 3D datasets are widespread. The reduced size of 3D datasets is overcome by an appropriate 3D data augmentation to train our model. 3D ShapeNets are commonly used to recognize and analyse objects. To the best of the authors' knowledge, this is the first time they are used for face recognition. This research work focuses on the preprocessing step. Whatever the nature of the face image is (either 2D or 3D representation), and whatever the acquisition conditions are, a 3D regular mesh of each input face image is first generated. Furthermore, meshes are converted to voxels to get the occupancy grid across all possible views. Finally, 3D ShapeNets are trained and recognition tests are performed. Indeed, 3D ShapeNets prove the efficiency and efficacy of 3D shape analysis task in 3D face recognition. The experimental results show that 3D face recognition using deep 3D CNNs such as 3D ShapeNets leads to significant improvement over the state-of-the-art performance on LFPW, BU3DFE, and FRAV3D datasets, with competitive recognition rates of 94.25%, 97.9% and 98.31% respectively.

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Section: D Face Recognition Based On 3d Shapenets a Overall Pipelinementioning

confidence: 99%

Section: B Data Preprocessing 1) 3d Face Reconstructionmentioning

confidence: 99%

Section: B Data Preprocessing 1) 3d Face Reconstructionmentioning

confidence: 99%

“…So, correction consisting in missing parts reconstruction and meshing is made. This correction is detailed in our previous research work [52].…”

Section: Figure 5 Facial Surface Extraction Using Region Growing Segm...mentioning

confidence: 99%

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Face ShapeNets for 3D Face Recognition

et al. 2023

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“…Precise recognition of landmarks is performed using Mediapipe [31], [32], [33], [34], [35] which is mainly used in real-time applications such as emotion detection, Parkinson's disease detection, driver drowsiness detection, and earlystage autism screening [36], [37], [38], [39], [40], [41]. It estimates 468 landmarks in real-time to improve the accuracy of the face recognition system (FRS) compared to other existing approaches, such as Multi-Task Cascaded Convolutional Networks (MTCNN) [42], [43] and Digital Library (DLIB) [44], [45]. After that, Euclidean and Geodesic distances were measured from the selected landmark points to extract the features.…”

Section: Introductionmentioning

confidence: 99%

2D-3D Facial Image Analysis for Identification of Facial Features Using Machine Learning Algorithms With Hyper-Parameter Optimization for Forensics Applications

et al. 2023

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Recognizing a face is a remarkable process that humans naturally use. Computer vision has tried to resemble this ability of human vision as a biometric tool to identify humans. Commercial and law enforcement applications are increasingly using face recognition technology to identify people. Currently, it is one of the most sought-after detection methods used in forensics for criminal identification purposes. Owing to similarities in the appearance of certain faces, especially in criminal cases, this problem poses a great challenge in forensic investigation and detection. The novelty of this work lies in the development of a framework for face recognition using 2D facial images gathered from various sources to generate a 3D face mesh using 468 MediaPipe landmarks which detects multiple faces in real-time. This leads to the generation of input feature vectors being formulated utilizing Euclidean/Geodesic distances and their ratios between the landmarks. These feature vectors are then trained into various classifiers that can provide the correct matching decision in an unrestricted environment such as large pose, expression, and occlusion variations. These quantitative similarity measures can then be presented as statistical evidence to identify criminals in forensic investigations. This two-dimensional to three-dimensional annotation provides a higher quality of threedimensional reconstructed face models without the need for any additional three-dimensional morphable models. The proposed methods were validated and tested to achieve comparable recognition performance using hyperparameter optimization. Regarding accuracy, the statistical results show that Extreme gradient boosting is the best classification model that provides the best accuracy (78%) for predicting facial images compared with Adaptive Boosting (77%), Random Forest (75%), Bernoulli Naive Bayes (68%), Decision Tree (57%), Logistic Regression (71%), Light Gradient Boosting Model (58%), Extra Tree Classifier (57%), Support Vector Machine (58%), and Nearest Centroid (62%) classifiers which can be further increased by considering a greater number of images in the dataset implying at the potential of further research for scale-up implementation.

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68 Landmarks are Efficient for 3D Face Alignment: What about More? 3D Face Alignment Method Applied to Face Recognition

Abstract: Presentation of 3D Face Alignment method with application to Face Recognition

Cited by 2 publications

References 54 publications

Face ShapeNets for 3D Face Recognition

Face ShapeNets for 3D Face Recognition

2D-3D Facial Image Analysis for Identification of Facial Features Using Machine Learning Algorithms With Hyper-Parameter Optimization for Forensics Applications

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