scholarly journals Detecting Facial Region and Landmarks at Once via Deep Network

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5360
Author(s):  
Taehyung Kim ◽  
Jiwon Mok ◽  
Euichul Lee
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Single Shot ◽  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Region ◽  
Facial Landmarks ◽  
Detection Model ◽  
Facial Landmark ◽  
Shot Detection

For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is used. Therefore, we propose a model that can simultaneously detect a face region and a landmark without performing the face detection step before landmark detection. The proposed single-shot detection model is based on the framework of YOLOv3, a one-stage object detection method, and the loss function and structure are altered to learn faces and landmarks at the same time. In addition, EfficientNet-B0 was utilized as the backbone network to increase processing speed and accuracy. The learned database used 300W-LP with 64 facial landmarks. The average normalized error of the proposed model was 2.32 pixels. The processing time per frame was about 15 milliseconds, and the average precision of face detection was about 99%. As a result of the evaluation, it was confirmed that the single-shot detection model has better performance and speed than the previous methods. In addition, as a result of using the COFW database, which has 29 landmarks instead of 64 to verify the proposed method, the average normalization error was 2.56 pixels, which was also confirmed to show promising performance.

scholarly journals Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096757
Author(s):  
Li Mao ◽  
Delei Zhang ◽  
Youming Chen ◽  
Tao Zhang ◽  
Xiaoning Song
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Sparse Representation ◽  
Image Feature ◽  
Collaborative Representation ◽  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Landmarks ◽  
Face Classification ◽  
Facial Landmark

Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

scholarly journals Facial Landmark Detection Using Generative Adversarial Network Combined with Autoencoder for Occlusion

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hongzhe Liu ◽  
Weicheng Zheng ◽  
Cheng Xu ◽  
Teng Liu ◽  
Min Zuo
Keyword(s):  
Feature Maps ◽  
Landmark Detection ◽  
Generative Adversarial Network ◽  
Mutual Cooperation ◽  
Facial Landmark Detection ◽  
Detection Model ◽  
Adversarial Network ◽  
Face Region ◽  
Facial Landmark ◽  
Facial Shape

The performance of the facial landmark detection model will be in trouble when it is under occlusion condition. In this paper, we present an effective framework with the objective of addressing the occlusion problem for facial landmark detection, which includes a generative adversarial network with improved autoencoders (GAN-IAs) and deep regression networks. In this model, GAN-IA can restore the occluded face region by utilizing skip concatenation among feature maps to keep more details. Meanwhile, self-attention mechanism that is effective in modeling long-range dependencies is employed to recover harmonious images for occluded faces. Deep regression networks are used to learn a nonlinear mapping from facial appearance to facial shape. Benefited from the mutual cooperation of GAN-IA and deep regression networks, a robust facial landmark detection model is achieved for the occlusion problem and the performance of the model achieves obviously improvement on challenging datasets.

scholarly journals TFW: Annotated Thermal Faces in the Wild Dataset

2021 ◽  
Author(s):  
Askat Kuzdeuov ◽  
Dana Aubakirova ◽  
Darina Koishigarina ◽  
Hüseyin Atakan Varol
Keyword(s):  
Face Detection ◽  
Facial Landmark Detection ◽  
Facial Landmarks ◽  
Detection Model ◽  
Thermal Cameras ◽  
Benchmark Datasets ◽  
In The Wild ◽  
Bounding Boxes ◽  
Thermal Face ◽  
Detection And Localization

Face detection and localization of facial landmarks are the primary steps in building many face applications in computer vision. Numerous algorithms and benchmark datasets have been proposed to develop accurate face and facial landmark detection models in the visual domain. However, varying illumination conditions still pose challenging problems. Thermal cameras can address this problem because of their operation in longer wavelengths. However, thermal face detection and localization of facial landmarks in the wild condition are overlooked. The main reason is that most of the existing thermal face datasets have been collected in controlled environments. In addition, many of them contain no annotations of face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,202 images of 145 subjects, collected in both controlled and wild conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. To show the efficacy of our dataset, we evaluated these models on the RWTH-Aachen thermal face dataset in addition to our test set. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis. <br>

scholarly journals TFW: Annotated Thermal Faces in the Wild Dataset

2021 ◽  
Author(s):  
Askat Kuzdeuov ◽  
Dana Aubakirova ◽  
Darina Koishigarina ◽  
Hüseyin Atakan Varol
Keyword(s):  
Face Detection ◽  
Facial Landmark Detection ◽  
Facial Landmarks ◽  
Detection Model ◽  
Thermal Cameras ◽  
Benchmark Datasets ◽  
In The Wild ◽  
Bounding Boxes ◽  
Thermal Face ◽  
Detection And Localization

Face detection and localization of facial landmarks are the primary steps in building many face applications in computer vision. Numerous algorithms and benchmark datasets have been proposed to develop accurate face and facial landmark detection models in the visual domain. However, varying illumination conditions still pose challenging problems. Thermal cameras can address this problem because of their operation in longer wavelengths. However, thermal face detection and localization of facial landmarks in the wild condition are overlooked. The main reason is that most of the existing thermal face datasets have been collected in controlled environments. In addition, many of them contain no annotations of face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,202 images of 145 subjects, collected in both controlled and wild conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. To show the efficacy of our dataset, we evaluated these models on the RWTH-Aachen thermal face dataset in addition to our test set. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis. <br>

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