However, deploying such high performing models to resource constraint devices or real-time applications is challenging.
Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently.
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.
Person re-identification (re-ID) aims to accurately re- trieve a person from a large-scale database of images cap- tured across multiple cameras.
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.