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Chi Lin

Researcher at University of Southern California

Publications -  8
Citations -  186

Chi Lin is an academic researcher from University of Southern California. The author has contributed to research in topics: Gesture recognition & Convolutional neural network. The author has an hindex of 5, co-authored 8 publications receiving 122 citations. Previous affiliations of Chi Lin include Macau University of Science and Technology.

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Proceedings ArticleDOI

Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges

TL;DR: This second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on "multimedia challenges beyond visual analysis", has considerably improved, and the performances considerably improved compared to the first round.
Proceedings ArticleDOI

Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019

TL;DR: An overview of the Chalearn LAP multi-modal face anti-spoofing attack detection challenge, including its design, evaluation protocol and a summary of results, is presented.
Proceedings ArticleDOI

Large-Scale Isolated Gesture Recognition Using a Refined Fused Model Based on Masked Res-C3D Network and Skeleton LSTM

TL;DR: A novel ensemble method to explore deep spatio-temporal features using 3D Convolutional Neural Networks (CNNs) with residual architecture (Res-C3D) and build a time-series model with skeleton information based on Long Short Term Memory network (LSTM).
Journal ArticleDOI

ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition.

TL;DR: This article proposes a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points, and introduces the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition.
Journal ArticleDOI

Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis

TL;DR: A task-oriented feature-fused network (TFN) is proposed for simultaneously solving face detection, landmark localization, and attribute analysis, and the experimental results suggest that the TFN outperforms several multitask models on the JFA dataset.