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Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
TLDR
A new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters, is proposed.
Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary
TLDR
Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages.
Very deep convolutional neural network based image classification using small training sample size
TLDR
The results show that the very deep CNN can be used to fit small datasets with simple and proper modifications and don't need to re-design specific small networks.
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
TLDR
A Cross-Age LFW is constructed which deliberately searches and selects 3,000 positive face pairs with age gaps to add aging process intra-class variance and evaluate several metric learning and deep learning methods on the new database.
Cross-Pose LFW : A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments
Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification. Recently, due to big data driven machine learning methods, the performance on
Deep Facial Expression Recognition: A Survey
TLDR
This survey provides a comprehensive review on deep FER, including datasets and algorithms that provide insights into overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias.
Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network
TLDR
A deep information maximization adaptation network (IMAN) is proposed to alleviate this bias by using Caucasian as source domain and other races as target domains and learns the discriminative target representations at cluster level.
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition
TLDR
A new deep locality-preserving convolutional neural network (DLP-CNN) method that aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatter is proposed.
In Defense of Sparsity Based Face Recognition
TLDR
Results on AR, FERET and FRGC databases validate that, if the proposed prototype plus variation representation model is applied, sparse coding plays a crucial role in face recognition, and performs well even when the dictionary bases are collected under uncontrolled conditions and only a single sample per classes is available.
Discriminative Multimetric Learning for Kinship Verification
TLDR
Experimental results show the effectiveness of the proposed discriminative multimetric learning method for kinship verification via facial image analysis over the existing single-metric and multimetricLearning methods.
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