Towards completely rotated Simplified Gabor Wavelets for fast facial feature point detection

Author(s):  
Axel Panning ◽  
Ayoub Al-Hamadi ◽  
Bernd Michaelis
Author(s):  
Masahiko Minamoto ◽  
Hidaka Sato ◽  
Takahiro Kanno ◽  
Tetsuro Miyazaki ◽  
Toshihiro Kawase ◽  
...  

2018 ◽  
Vol 275 ◽  
pp. 50-65 ◽  
Author(s):  
Nannan Wang ◽  
Xinbo Gao ◽  
Dacheng Tao ◽  
Heng Yang ◽  
Xuelong Li

Author(s):  
Shyota Shindo ◽  
Takaaki Goto ◽  
Tadaaki Kirishima ◽  
Kensei Tsuchida

<p>Detection of facial feature points is an important technique used for biometric authentication and facial expression estimation. A facial feature point is a local point indicating both ends of the eye, holes of the nose, and end points of the mouth in the face image. Many researches on face feature point detection have been done so far, but the accuracy of facial organ point detection is improving by the approach using<br />Convolutional Neural Network (CNN). However, CNN not only takes time to learn but also the neural network becomes a complicated model, so it is necessary to improve learning time and detection accuracy. In this research, the improvement of the detection accuracy of the learning speed is improved by increasing the convolution layer.</p>


2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Shuqiu Tan ◽  
Dongyi Chen ◽  
Chenggang Guo ◽  
Zhiqi Huang

Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.


2016 ◽  
Vol 25 (4) ◽  
pp. 043028 ◽  
Author(s):  
Yong Cheng ◽  
Zuoyong Li ◽  
Liangbao Jiao ◽  
Hong Lu ◽  
Xuehong Cao

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