Multistage Model for Robust Face Alignment Using Deep Neural Networks

Learning Robust Shape-Indexed Features for Facial Landmark Detection

In facial landmark detection, extracting shape-indexed features is widely applied in existing methods to impose shape constraint over landmarks. Commonly, these methods crop shape-indexed patches surrounding landmarks of a given initial shape. All landmarks are then detected jointly based on these patches, with shape constraint naturally embedded in the regressor. However, there are still two remaining challenges that cause the degradation of these methods. First, the initial shape may seriously deviate from the ground truth when presented with a large pose, resulting in considerable noise in the shape-indexed features. Second, extracting local patch features is vulnerable to occlusions due to missing facial context information under severe occlusion. To address the issues above, this paper proposes a facial landmark detection algorithm named Sparse-To-Dense Network (STDN). First, STDN employs a lightweight network to detect sparse facial landmarks and forms a reinitialized shape, which can efficiently improve the quality of cropped patches when presented with large poses. Then, a group-relational module is used to exploit the inherent geometric relations of the face, which further enhances the shape constraint against occlusion. Our method achieves 4.64% mean error with 1.97% failure rate on COFW68 dataset, 3.48% mean error with 0.43% failure rate on 300 W dataset and 7.12% mean error with 11.61% failure rate on Masked 300 W dataset. The results demonstrate that STDN achieves outstanding performance in comparison to state-of-the-art methods, especially on occlusion datasets.

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.