Can We Extract 3D Biometrics from 2D Images for Facial Beauty Analysis?

Han, Wen-Ming; Chen, Fangmei; Sun, Fuming

doi:10.1145/3436369.3436476

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…By data‐driven geometry extraction and incremental feature selection, we 23 incorporated the most discriminative subset of ratios, angles, and inclinations in the evaluation of 2.5D facial aesthetics. Han et al 24 reconstructed 3D faces from 2D images to extract 2D–3D biometrics, which were demonstrated more robust than 2D direct features.…”

Section: Introductionmentioning

confidence: 99%

3D facial attractiveness prediction based on deep feature fusion

Liu,

Huang,

Zhou

et al. 2023

Computer Animation & Virtual

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Facial attractiveness prediction is an important research topic in the computer vision community. It not only contributes to the development of interdisciplinary research in psychology and sociology, but also provides fundamental technical support for applications like aesthetic medicine and social media. With the advances in 3D data acquisition and feature representation, this paper aims to investigate the facial attractiveness from deep learning and three‐dimensional perspectives. The 3D faces are first processed to unwrap the texture images and refine the raw meshes. The feature extraction networks for texture, point cloud, and mesh are then delicately designed, considering the characteristics of different types of data. A more discriminative face representation is derived by feature fusion for the final attractiveness prediction. During network training, the cyclical learning rate with an improved range test is introduced, so as to alleviate the difficulty in hyperparameter setting. Extensive experiments are conducted on a 3D FAP benchmark, where the results demonstrate the significance of deep feature fusion and enhanced learning rate in cooperatively facilitating the performance. Specifically, the fusion of texture image and point cloud achieves the best overall prediction, with PC, MAE, and RMSE of 0.7908, 0.4153, and 0.5231, respectively.

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Section: Introductionmentioning

confidence: 99%

3D facial attractiveness prediction based on deep feature fusion

Liu,

Huang,

Zhou

et al. 2023

Computer Animation & Virtual

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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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