Facial Region Segmentation to Enhance Orientation Invariability and Geometrical Information for Face Detection Based on Histogram of Oriented Gradients (HoGs)

2017 ◽  
Vol 15 (6) ◽  
pp. 77-85
Author(s):  
Hee-Jae Lee ◽  
David Lee ◽  
Sang-Goog Lee
Keyword(s):  
Face Detection ◽  
Histogram Of Oriented Gradients ◽  
Region Segmentation ◽  
Facial Region ◽  
Geometrical Information

scholarly journals Comparison of Viola-Jones Haar Cascade Classifier and Histogram of Oriented Gradients (HOG) for face detection

2020 ◽  
Vol 732 ◽  
pp. 012038 ◽  
Author(s):  
C Rahmad ◽  
R A Asmara ◽  
D R H Putra ◽  
I Dharma ◽  
H Darmono ◽  
...  
Keyword(s):  
Face Detection ◽  
Histogram Of Oriented Gradients ◽  
Cascade Classifier ◽  
Haar Cascade

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.

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