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.
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.
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
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
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
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.