1 code implementation • 3 Apr 2017 • Lin Xiong, Jayashree Karlekar, Jian Zhao, Yi Cheng, Yan Xu, Jiashi Feng, Sugiri Pranata, ShengMei Shen
In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge.
no code implementations • NeurIPS 2017 • Jian Zhao, Lin Xiong, Panasonic Karlekar Jayashree, Jianshu Li, Fang Zhao, Zhecan Wang, Panasonic Sugiri Pranata, Panasonic Shengmei Shen, Shuicheng Yan, Jiashi Feng
In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses.
Ranked #1 on Face Verification on IJB-A
no code implementations • CVPR 2018 • Jian Zhao, Yu Cheng, Yan Xu, Lin Xiong, Jianshu Li, Fang Zhao, Karlekar Jayashree, Sugiri Pranata, ShengMei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
1 code implementation • 2 Sep 2018 • Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Haochong Lan, Fang Zhao, Lin Xiong, Yan Xu, Jianshu Li, Sugiri Pranata, ShengMei Shen, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Ranked #1 on Age-Invariant Face Recognition on MORPH Album2
no code implementations • 23 Jul 2021 • Shasha Mao, GuangHui Shi, Licheng Jiao, Shuiping Gou, Yangyang Li, Lin Xiong, Boxin Shi
Based on this, we propose a new method that amends the label distribution of each facial image by leveraging correlations among expressions in the semantic space.
Facial Expression Recognition Facial Expression Recognition (FER)
1 code implementation • CVPR 2022 • Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, Lin Xiong
This approach suffers from both unsatisfactory accuracy of pseudo labels due to the presence of domain shift and limited use of target domain training data.
no code implementations • 26 Mar 2022 • GuangHui Shi, Shasha Mao, Shuiping Gou, Dandan Yan, Licheng Jiao, Lin Xiong
In the proposed method, two parts are constructed based on facial local and non-local information respectively, where an ensemble of multiple local networks are proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region.
Facial Expression Recognition Facial Expression Recognition (FER)