Robust facial landmark detection by cross-order cross-semantic deep network

Automatic labeling of facial zones for digital clinical application: An ensemble of semantic segmentation models

INTRODUCTION

The application of artificial intelligence to facial aesthetics has been limited by the inability to discern facial zones of interest, as defined by complex facial musculature and underlying structures. Although semantic segmentation models (SSMs) could potentially overcome this limitation, existing facial SSMs distinguish only three to nine facial zones of interest.

METHODS

We developed a new supervised SSM, trained on 669 high-resolution clinical-grade facial images; a subset of these images was used in an iterative process between facial aesthetics experts and manual annotators that defined and labeled 33 facial zones of interest.

RESULTS

Because some zones overlap, some pixels are included in multiple zones, violating the one-to-one relationship between a given pixel and a specific class (zone) required for SSMs. The full facial zone model was therefore used to create three sub-models, each with completely non-overlapping zones, generating three outputs for each input image that can be treated as standalone models. For each facial zone, the output demonstrating the best Intersection Over Union (IOU) value was selected as the winning prediction.

CONCLUSIONS

The new SSM demonstrates mean IOU values superior to manual annotation and landmark analyses, and it is more robust than landmark methods in handling variances in facial shape and structure.

A Facial Landmark Detection Method Based on Deep Knowledge Transfer

Facial landmark detection is a crucial preprocessing step in many applications that process facial images. Deep-learning-based methods have become mainstream and achieved outstanding performance in facial landmark detection. However, accurate models typically have a large number of parameters, which results in high computational complexity and execution time. A simple but effective facial landmark detection model that achieves a balance between accuracy and speed is crucial. To achieve this, a lightweight, efficient, and effective model is proposed called the efficient face alignment network (EfficientFAN) in this article. EfficientFAN adopts the encoder-decoder structure, with a simple backbone EfficientNet-B0 as the encoder and three upsampling layers and convolutional layers as the decoder. Moreover, deep dark knowledge is extracted through feature-aligned distillation and patch similarity distillation on the teacher network, which contains pixel distribution information in the feature space and multiscale structural information in the affinity space of feature maps. The accuracy of EfficientFAN is further improved after it absorbs dark knowledge. Extensive experimental results on public datasets, including 300 Faces in the Wild (300W), Wider Facial Landmarks in the Wild (WFLW), and Caltech Occluded Faces in the Wild (COFW), demonstrate the superiority of EfficientFAN over state-of-the-art methods.

Precise Facial Landmark Detection by Reference Heatmap Transformer

Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the face image is suffering from large poses, heavy occlusions and complicated illuminations, they cannot learn discriminative feature representations and effective facial shape constraints, nor can they accurately predict the value of each element in the landmark heatmap, limiting their detection accuracy. To address this problem, we propose a novel Reference Heatmap Transformer (RHT) by introducing reference heatmap information for more precise facial landmark detection. The proposed RHT consists of a Soft Transformation Module (STM) and a Hard Transformation Module (HTM), which can cooperate with each other to encourage the accurate transformation of the reference heatmap information and facial shape constraints. Then, a Multi-Scale Feature Fusion Module (MSFFM) is proposed to fuse the transformed heatmap features and the semantic features learned from the original face images to enhance feature representations for producing more accurate target heatmaps. To the best of our knowledge, this is the first study to explore how to enhance facial landmark detection by transforming the reference heatmap information. The experimental results from challenging benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art methods in the literature.

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.