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3D-Aided Deep Pose-Invariant Face Recognition

Jian Zhao, Lin Xiong, Yu Cheng, Yi Cheng, Jianshu Li, Li Zhou, Yan Xu, Jayashree Karlekar, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan (+1 others)
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
To mitigate this gap, we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model (3D-PIM), which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel  ...  Specifically, 3D-PIM incorporates a simulator with the aid of a 3D Morphable Model (3D MM) to obtain shape and appearance prior for accelerating face normalization learning, requiring less training data  ...  Figure 2 : 2 3D-Aided Deep Pose Invariant Model (3D-PIM) for pose-invariant face recognition.  ... 
doi:10.24963/ijcai.2018/165 dblp:conf/ijcai/ZhaoXCCLZXKPSXY18 fatcat:2bfsdg742veahofrzjsped5zje

When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition [article]

Xiang Xu and Pengfei Dou and Ha A. Le and Ioannis A. Kakadiaris
2017 arXiv   pre-print
This paper presents a pose-invariant 3D-aided 2D face recognition system (UR2D) that is robust to pose variations as large as 90? by leveraging deep learning technology.  ...  It fills a gap by providing a 3D-aided 2D face recognition system that has compatible results with 2D face recognition systems using deep learning techniques.  ...  System Design UR2D is a 3D-aided 2D face recognition system designed for pose-invariant face recognition.  ... 
arXiv:1709.06532v1 fatcat:tspafbyzmvdjbleb2f4vgw2bxu

Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization [article]

Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, Zhenan Sun
2018 arXiv   pre-print
Exhaustive experiments on both controlled and uncontrolled environments demonstrate that the proposed method not only boosts the performance of pose-invariant face recognition but also dramatically improves  ...  This paper proposes a High Fidelity Pose Invariant Model (HF-PIM) to produce photographic and identity-preserving results.  ...  Inspired by [30] , DA-GAN [36] , which acts as a 2D face image refiner, can be employed for pose-invariant face recognition.  ... 
arXiv:1806.08472v2 fatcat:pflnq7o74be6vgxybzkzhb7y6a

Histogram-Based CRC for 3D-Aided Pose-Invariant Face Recognition

Liang Shi, Xiaoning Song, Tao Zhang, Yuquan Zhu
2019 Sensors  
To address this issue, in this paper, we design a new CRC method using histogram statistical measurement (H-CRC) combined with a 3D morphable model (3DMM) for pose-invariant face classification.  ...  Second, we use a histogram-based metric learning to evaluate the most similar neighbours of the test sample, which aims to obtain ideal result for pose-invariant face recognition using the designed histogram-based  ...  Conclusions In this paper, a novel histogram-based CRC for 3D-aided pose-invariant face recognition is developed.  ... 
doi:10.3390/s19040759 fatcat:5lbwcblhonbujdujlnqfa23lf4

Towards Large-Pose Face Frontalization in the Wild [article]

Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker
2017 arXiv   pre-print
Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments.  ...  We propose a novel deep 3D Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial Network (GAN), termed as FF-GAN, to generate neutral head pose face images.  ...  Pose-Invariant Feature Representation While face frontalization may be considered an image-level poseinvariant representation, feature representations invariant to pose have also been a mainstay of face  ... 
arXiv:1704.06244v3 fatcat:dkzajxxy2fhmnfwxjodvywy4va

Joint 3D facial shape reconstruction and texture completion from a single image

Xiaoxing Zeng, Zhelun Wu, Xiaojiang Peng, Yu Qiao
2021 Computational Visual Media  
We examine our methods on 3D reconstruction tasks as well as face frontalization and pose invariant face recognition tasks, using both in-the-lab datasets (MICC, MultiPIE) and in-the-wild datasets (CFP  ...  AbstractRecent years have witnessed significant progress in image-based 3D face reconstruction using deep convolutional neural networks.  ...  We would like to thank Yu Deng et al. for their Deep 3D Face work in 3D face analysis, whose contribution to this field permitted our further study.  ... 
doi:10.1007/s41095-021-0238-4 fatcat:trtdm3c7ljd3pb7lvxlmxq6dhu

Pose-robust face signature for multi-view face recognition

Pengfei Dou, Lingfeng Zhang, Yuhang Wu, Shishir K. Shah, Ioannis A. Kakadiaris
2015 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)  
We propose a novel framework for multi-view face recognition based on extracting and matching pose-robust face signatures from 2D images.  ...  Despite the great progress achieved in unconstrained face recognition, pose variations still remain a challenging and unsolved practical issue.  ...  The framework combines 3D-aided pose normalization and part-based face recognition and employs self-occlusion estimation to further enhance the local features and regularize the feature matching.  ... 
doi:10.1109/btas.2015.7358788 dblp:conf/btas/DouZWSK15 fatcat:6bxncscu6rhjdeb2gesjhdmqai

A deep learning framework for football match prediction

Md. Ashiqur Rahman
2020 SN Applied Sciences  
An efficient framework is developed by deep neural networks (DNNs) and artificial neural network (ANNs) for predicting the outcomes of football matches.  ...  The outcome of this hypothesis can be derived that deep learning may be used for successfully predicting the outcomes of football matches or any other sporting events.  ...  Deeply Learned pose invariant image analysis with applications in 3D face recognition, a novel approach based on deeply learned pose invariant image analysis with applications in 3D face recognition is  ... 
doi:10.1007/s42452-019-1821-5 fatcat:emsn7afwhfdu5eoidsdbjrzh2e

Efficient 3D morphable face model fitting

Guosheng Hu, Fei Yan, Josef Kittler, William Christmas, Chi Ho Chan, Zhenhua Feng, Patrik Huber
2017 Pattern Recognition  
A face recognition system integrating ESO to provide a pose and illumination invariant solution compares favourably with other state-of-the-art methods.  ...  In addition, we demonstrate its merits in the context of a 3D-assisted 2D face recognition system which detects landmarks automatically and extracts both holistic and local features using a 3DMM.  ...  In particular, deep learning works well for pose-and illumination-invariant face recognition [11, 12] .  ... 
doi:10.1016/j.patcog.2017.02.007 fatcat:mpsnlvokmrehpiw2ylgzu3ik2q

PI-GAN: Learning Pose Independent representations for multiple profile face synthesis [article]

Hamed Alqahtani
2019 arXiv   pre-print
Generating a pose-invariant representation capable of synthesizing multiple face pose views from a single pose is still a difficult problem.  ...  As compared to traditional GAN, it consists of secondary encoder-decoder framework sharing weights from the primary structure and reconstructs the face with the original pose.  ...  Traditional methods model the 3D mean for frontal view synthesis [13, 25, 26] . Deep learning methods focus on preserving face information [5] . Yang et al.  ... 
arXiv:2001.00645v1 fatcat:pzuiun5ebnanpcr26vikzrtlvi

Real-time Facial Expression Recognition "In The Wild" by Disentangling 3D Expression from Identity [article]

Mohammad Rami Koujan, Luma Alharbawee, Giorgos Giannakakis, Nicolas Pugeault, Anastasios Roussos
2020 arXiv   pre-print
We construct a large-scale dataset of facial videos (FaceVid), rich in facial dynamics, identities, expressions, appearance and 3D pose variations.  ...  This paper proposes a novel method for human emotion recognition from a single RGB image.  ...  The 4DFAB dataset is a large-scale database of dynamic high-resolution 3D faces (more than 1.8M 3D face) with subjects displaying both spontaneous and posed facial expressions.  ... 
arXiv:2005.05509v1 fatcat:2l5k2iq7crdxpo6u5qpbhgfc2q

Facial Expression Recognition: A Review of Trends and Techniques

Olufisayo Ekundayo, Serestina Viriri
2021 IEEE Access  
Facial Expression Recognition (FER) is presently the aspect of cognitive and affective computing with the most attention and popularity, aided by its vast application areas.  ...  We also include studies on expression intensity estimation from the face.  ...  Deep learning models also explore 2D and 3D model fitting for pose normalisation. The deep learning model was able to synthesis frontal faces from the training of several multi-posed data.  ... 
doi:10.1109/access.2021.3113464 fatcat:hapy6t6ohneupiwh7meakzk3ma

Multi-set Canonical Correlation Analysis for 3D Abnormal Gait Behaviour Recognition based on Virtual Sample Generation

Jian Luo, Tardi Tjahjadi
2020 IEEE Access  
INDEX TERMS 3D body modelling, abnormal gait behaviour recognition, long short-term memory model, multi-set canonical correlation analysis.  ...  These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis.  ...  ., view-invariant face features), and face expression recognition theory [18] .  ... 
doi:10.1109/access.2020.2973898 fatcat:oxark47ikrhg7k3dg7zyedar6y

Facial Expression Recognition: A Survey

Yunxin Huang, Fei Chen, Shaohe Lv, Xiaodong Wang
2019 Symmetry  
Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject  ...  We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches.  ...  Hence, GAN-based models conducive for pose-invariant and identity-invariant expression recognition.  ... 
doi:10.3390/sym11101189 fatcat:wriubaqdobcazplwaayluxowlu

A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Shuxin Ouyang, Timothy Hospedales, Yi-Zhe Song, Xueming Li, Chen Change Loy, Xiaogang Wang
2016 Image and Vision Computing  
Heterogeneous face recognition (HFR) refers to matching face imagery across different domains.  ...  A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets being established in recent years.  ...  [14] compared two 3D-aided face recognition where the 3D model is used for either image normalisation or rendering.  ... 
doi:10.1016/j.imavis.2016.09.001 fatcat:hy666szkk5bgfoazyxgwy6hli4
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