10 citations · 23 across the 5 of their papers we have counts for
5 papers
Gallery Sampling for Robust and Fast Face Identification
Myung-cheol Roh, Pyoung-gang Lim, Jongju Shin
Deep learning methods have been achieved brilliant results in face recognition. One of the important tasks to improve the performance is to collect and label images as many as poss…
BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition
Yonghyun Kim, Wonpyo Park, Jongju Shin
The datasets of face recognition contain an enormous number of identities and instances. However, conventional methods have difficulty in reflecting the entire distribution of the…
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
Yonghyun Kim, Wonpyo Park, Myung-Cheol Roh +1
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly enc…
Uncorrelated Feature Encoding for Faster Image Style Transfer
Minseong Kim, Jongju Shin, Myung-Cheol Roh +1
Recent fast style transfer methods use a pre-trained convolutional neural network as a feature encoder and a perceptual loss network. Although the pre-trained network is used to ge…
Face Alignment Robust to Pose, Expressions and Occlusions
Vishnu Naresh Boddeti, Myung-Cheol Roh, Jongju Shin +2
We propose an Ensemble of Robust Constrained Local Models for alignment of faces in the presence of significant occlusions and of any unknown pose and expression. To account for pa…