研究目的
To provide a review of the current facial landmarking literature, outlining the significant progress that has been made in the field from classical generative methods to more modern techniques such as sophisticated deep neural network architectures, and to report benchmarks across publicly available datasets.
研究成果
Recent techniques, particularly multi-stage convolutional neural networks, have significantly improved facial landmarking performance, achieving near-human accuracy in controlled environments. However, challenges remain in highly variable conditions. Future progress requires larger and more diverse datasets, improved face detection methods, and advanced modeling techniques to reduce overfitting and enhance generalization.
研究不足
The review focuses on 2D image-based methods and does not cover 3D facial landmarking. The experimental comparisons are indirect due to differences in training data and methodologies across studies. Performance of models may vary in uncontrolled environments with high variability in pose, expression, and lighting.
1：Experimental Design and Method Selection:
The review is structured around a generic model construction process with five stages: objective definition, dataset selection, extraction of regions of interest, model definition, and model training and evaluation. Experimental components are included in stages 2, 3, and 4 to demonstrate aspects such as ground truth variability and performance of off-the-shelf face detectors.
2：Sample Selection and Data Sources:
Publicly available datasets including BioID, HELEN, MUCT, 300W, and Menpo are used, with specific landmark configurations like MULTI-PIE 68 points.
3：List of Experimental Equipment and Materials:
High-performance computing facilities (Dell PowerEdge R630 and C6320 Server nodes) are used for training and testing models. Software tools include OpenCV, Dlib, and MenpoFit.
4：Experimental Procedures and Operational Workflow:
For face detection, OpenCV and Dlib implementations are applied to datasets with default parameters. For landmarking, holistic and patch-based Active Appearance Models (AAMs) are trained and evaluated using normalised root mean squared error (NRMSE) and cumulative error distribution (CED) curves.
5：Data Analysis Methods:
Performance is measured using NRMSE and CED plots. Statistical analysis includes comparing results with published literature.
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Nvidia GTX 1080TI GPU
GTX 1080TI
Nvidia
Graphics processing unit mentioned in the context of computational power for deep learning methods.
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Dell PowerEdge R630
R630
Dell
High-performance computing server used for training and testing models in the experiments.
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Dell PowerEdge C6320
C6320
Dell
High-performance computing server used for training and testing models in the experiments.
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OpenCV
OpenCV
Computer vision library used for implementing face detection algorithms (Viola-Jones method).
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Dlib
Dlib
Machine learning toolkit used for HOG-based face detection.
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MenpoFit
Menpo
Python-based open-source implementation of active appearance models (AAMs) used for facial landmarking.
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Amazon Mechanical Turk
Amazon
Platform used for conducting a ground truth variability study with human annotators.
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turkmarker
Web-based tool for manual annotation of facial landmarks in the variability study.
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GitLab
GitLab
Platform for hosting and sharing source code and data related to the experiments.
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Docker
Docker
Containerization platform used for providing executable forms of the source code.
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Kinect
Microsoft
3D scanner mentioned as an example for 3D facial models, though not used in the paper's experiments.
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