Direct Shape Regression Networks for End-to-End Face Alignment

2018 ◽  
Author(s):  
Xin Miao ◽  
Xiantong Zhen ◽  
Xianglong Liu ◽  
Cheng Deng ◽  
Vassilis Athitsos ◽  
...  
Keyword(s):  
Face Alignment ◽  
Shape Regression ◽  
End To End

scholarly journals Multi-feature shape regression for face alignment

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Wei-Jong Yang ◽  
Yi-Chen Chen ◽  
Pau-Choo Chung ◽  
Jar-Ferr Yang
Keyword(s):  
Face Alignment ◽  
Shape Regression

Improved explicit shape regression face alignment algorithm

2018 ◽  
Author(s):  
Lin Cong ◽  
Zhang Ting ◽  
Lv Chongshan ◽  
Wu Wei ◽  
Zhan Xiang ◽  
...  
Keyword(s):  
Face Alignment ◽  
Alignment Algorithm ◽  
Shape Regression

Face Alignment by Explicit Shape Regression

2013 ◽  
Vol 107 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Xudong Cao ◽  
Yichen Wei ◽  
Fang Wen ◽  
Jian Sun
Keyword(s):  
Face Alignment ◽  
Shape Regression

scholarly journals Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment

2016 ◽  
Author(s):  
George Trigeorgis ◽  
Patrick Snape ◽  
Mihalis A. Nicolaou ◽  
Epameinondas Antonakos ◽  
Stefanos Zafeiriou
Keyword(s):  
Descent Method ◽  
Face Alignment ◽  
Recurrent Process ◽  
End To End

scholarly journals The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

2022 ◽  
Author(s):  
Hang Du ◽  
Hailin Shi ◽  
Dan Zeng ◽  
Xiao-Ping Zhang ◽  
Tao Mei
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Face Alignment ◽  
Face Representation ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face ◽  
End To End

Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to the canonical view and crop them with a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the aligned face for recognition. Nowadays, all of the three elements are fulfilled by the technique of deep convolutional neural network. In this survey article, we present a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved the capability of them. To start with, we present an overview of the end-to-end deep face recognition. Then, we review the advance of each element, respectively, covering many aspects such as the to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. Also, we provide a detailed discussion about the effect of each element on its subsequent elements and the holistic system. Through this survey, we wish to bring contributions in two aspects: first, readers can conveniently identify the methods which are quite strong-baseline style in the subcategory for further exploration; second, one can also employ suitable methods for establishing a state-of-the-art end-to-end face recognition system from scratch.

scholarly journals Face Alignment in Thermal Infrared Images Using Cascaded Shape Regression

2021 ◽  
Vol 18 (4) ◽  
pp. 1776
Author(s):  
Kent Nagumo ◽  
Tomohiro Kobayashi ◽  
Kosuke Oiwa ◽  
Akio Nozawa
Keyword(s):  
Skin Temperature ◽  
Real Time ◽  
Region Of Interest ◽  
Thermal Infrared ◽  
Appearance Model ◽  
Face Alignment ◽  
Infrared Images ◽  
Time Performance ◽  
Thermal Infrared Images ◽  
Shape Regression

The evaluation of physiological and psychological states using thermal infrared images is based on the skin temperature of specific regions of interest, such as the nose, mouth, and cheeks. To extract the skin temperature of the region of interest, face alignment in thermal infrared images is necessary. To date, the Active Appearance Model (AAM) has been used for face alignment in thermal infrared images. However, computation using this method is costly, and it has a low real-time performance. Conversely, face alignment of visible images using Cascaded Shape Regression (CSR) has been reported to have high real-time performance. However, no studies have been reported on face alignment in thermal infrared images using CSR. Therefore, the objective of this study was to verify the speed and robustness of face alignment in thermal infrared images using CSR. The results suggest that face alignment using CSR is more robust and computationally faster than AAM.

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