82 citations · 188 across the 12 of their papers we have counts for
14 papers
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces
Joseph P. Robinson, Can Qin, Ming Shao +3
Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a la…
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild
Joseph P Robinson, Can Qin, Yann Henon +2
There are demographic biases present in current facial recognition (FR) models. To measure these biases across different ethnic and gender subgroups, we introduce our Balanced Face…
Automatic Face Understanding: Recognizing Families in Photos
Joseph P Robinson
We built the largest database for kinship recognition. The data were labeled using a novel clustering algorithm that used label proposals as side information to guide more accurate…
Multimodal In-bed Pose and Shape Estimation under the Blankets
Yu Yin, Joseph P. Robinson, Yun Fu
Humans spend vast hours in bed -- about one-third of the lifetime on average. Besides, a human at rest is vital in many healthcare applications. Typically, humans are covered by a…
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis
Yu Yin, Joseph P. Robinson, Songyao Jiang +3
Advances in face rotation, along with other face-based generative tasks, are more frequent as we advance further in topics of deep learning. Even as impressive milestones are achie…
Families In Wild Multimedia: A Multimodal Database for Recognizing Kinship
Joseph P. Robinson, Zaid Khan, Yu Yin +2
Kinship, a soft biometric detectable in media, is fundamental for a myriad of use-cases. Despite the difficulty of detecting kinship, annual data challenges using still-images have…
Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making
Joseph P Robinson, Ming Shao, Yun Fu
Kinship recognition is a challenging problem with many practical applications. With much progress and milestones having been reached after ten years - we are now able to survey the…
Dual-Attention GAN for Large-Pose Face Frontalization
Yu Yin, Songyao Jiang, Joseph P. Robinson +1
Face frontalization provides an effective and efficient way for face data augmentation and further improves the face recognition performance in extreme pose scenario. Despite recen…
Face Recognition: Too Bias, or Not Too Bias?
Joseph P Robinson, Gennady Livitz, Yann Henon +3
We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gend…
Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge
Joseph P. Robinson, Yu Yin, Zaid Khan +7
Recognizing Families In the Wild (RFIW): an annual large-scale, multi-track automatic kinship recognition evaluation that supports various visual kin-based problems on scales much…
Joint Super-Resolution and Alignment of Tiny Faces
Yu Yin, Joseph P. Robinson, Yulun Zhang +1
Super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. On the one hand, landmark localization could obtain higher accuracy with faces of high-re…
EV-Action: Electromyography-Vision Multi-Modal Action Dataset
Lichen Wang, Bin Sun, Joseph Robinson +2
Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and s…
Laplace Landmark Localization
Joseph P Robinson, Yuncheng Li, Ning Zhang +2
Landmark localization in images and videos is a classic problem solved in various ways. Nowadays, with deep networks prevailing throughout machine learning, there are revamped inte…
Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks
Joseph P. Robinson, Ming Shao, Yue Wu +1
We present the largest kinship recognition dataset to date, Families in the Wild (FIW). Motivated by the lack of a single, unified dataset for kinship recognition, we aim to provid…