Facial landmark detection by combining object detection and active shape model

2010 ◽  
Author(s):  
Yea-Shuan Huang ◽  
Ting-Chia Hsu ◽  
Fang-Hsuan Cheng
Keyword(s):  
Object Detection ◽  
Active Shape Model ◽  
Landmark Detection ◽  
Shape Model ◽  
Facial Landmark Detection ◽  
Facial Landmark ◽  
Active Shape

Multi-view facial landmark detection by using a 3D shape model

2016 ◽  
Vol 47 ◽  
pp. 60-70 ◽  
Author(s):  
Jan Čech ◽  
Vojtěch Franc ◽  
Michal Uřičář ◽  
Jiří Matas
Keyword(s):  
Landmark Detection ◽  
Shape Model ◽  
Facial Landmark Detection ◽  
3D Shape ◽  
Facial Landmark

An improved active shape model method for facial landmarking based on relative position feature

2017 ◽  
Vol 15 (01) ◽  
pp. 1750008 ◽  
Author(s):  
Hengxin Chen ◽  
Mingqi Gao ◽  
Bin Fang
Keyword(s):  
Relative Position ◽  
Shape Matching ◽  
Local Region ◽  
Active Shape Model ◽  
Shape Model ◽  
Matching Error ◽  
Model Method ◽  
Facial Landmark ◽  
Active Shape ◽  
Significant Performance

Active Shape Model (ASM) is a most effective method of facial landmarking. It employs two models, profile model and shape model, to match the position of facial landmark. In this paper, we introduce a new model based on relative position feature (RPF) in local region to improve ASM. We found the fact that landmarks with larger matching error have more shape matching displacement. So, in our method, RPF model is used to adjust the position of landmarks with more shape matching displacement in every matching iteration. STASM (Stacked ASM) is practical standard of ASM and is proved to be the best method of locating face landmarks. Our experiments on STASM show significant performance improving, especially on databases in which faces are partially blocked by glasses or artificial black square.

scholarly journals Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

2021 ◽  
Vol 11 (24) ◽  
pp. 11600
Author(s):  
Syed Farooq Ali ◽  
Ahmed Sohail Aslam ◽  
Mazhar Javed Awan ◽  
Awais Yasin ◽  
Robertas Damaševičius
Keyword(s):  
Head Rotation ◽  
Active Shape Model ◽  
Road Accidents ◽  
Scale Invariant ◽  
Shape Model ◽  
Facial Landmark Detection ◽  
Detection Algorithms ◽  
Boston University ◽  
Facial Landmark ◽  
Benchmark Datasets

Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, and driver’s head rotation, contribute significantly to causing a distraction. Many solutions have been proposed to address this problem; however, various aspects of it are still unresolved. The study proposes novel geometric and spatial scale-invariant features under a boosting framework for detecting a driver’s distraction due to the driver’s head panning. These features are calculated using facial landmark detection algorithms, including the Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). The proposed approach is compared with six existing state-of-the-art approaches using four benchmark datasets, including DrivFace dataset, Boston University (BU) dataset, FT-UMT dataset, and Pointing’04 dataset. The proposed approach outperforms the existing approaches achieving an accuracy of 94.43%, 92.08%, 96.63%, and 83.25% on standard datasets.

Human face shape classification method based on active shape model

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU
Keyword(s):  
Classification Method ◽  
Active Shape Model ◽  
Shape Classification ◽  
Human Face ◽  
Shape Model ◽  
Face Shape ◽  
Active Shape
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