Person Identification
18 papers with code • 2 benchmarks • 8 datasets
Datasets
11k Hands
iQIYI-VID
NSVA
MotionID: IMU all motions part3
MotionID: IMU specific motion
MotionID: IMU all motions part1
MotionID: IMU all motions part2
Latest papers
Subject-Aware Contrastive Learning for Biosignals
zacharycbrown/ssl_baselines_for_biosignal_feature_extraction
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Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100).
Ear2Face: Deep Biometric Modality Mapping
We have achieved very promising results, especially on the FERET dataset, generating visually appealing face images from ear image inputs.
Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions
Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications.
Weakly supervised discriminative feature learning with state information for person identification
KovenYu/state-information
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CVPR 2020
We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.
OPFython: A Python-Inspired Optimum-Path Forest Classifier
Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation.
A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities
We develop a system which generates summaries from seniors' indoor-activity videos captured by a social robot to help remote family members know their seniors' daily activities at home.
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
qinnzou/Gait-Recognition-Using-Smartphones
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Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network.
StoryGraphs: Visualizing Character Interactions as a Timeline
We present a novel way to automatically summarize and represent the storyline of a TV episode by visualizing character interactions as a chart.
