Search Results for author: Sixue Gong

Found 7 papers, 3 papers with code

Mitigating Face Recognition Bias via Group Adaptive Classifier

no code implementations CVPR 2021 Sixue Gong, Xiaoming Liu, Anil K. Jain

Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes.

Face Recognition

Jointly De-biasing Face Recognition and Demographic Attribute Estimation

1 code implementation ECCV 2020 Sixue Gong, Xiaoming Liu, Anil K. Jain

We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups.

Attribute Face Recognition

Recurrent Embedding Aggregation Network for Video Face Recognition

no code implementations26 Apr 2019 Sixue Gong, Yichun Shi, Anil K. Jain

Recurrent networks have been successful in analyzing temporal data and have been widely used for video analysis.

Face Recognition

Face Recognition: Primates in the Wild

1 code implementation24 Apr 2018 Debayan Deb, Susan Wiper, Alexandra Russo, Sixue Gong, Yichun Shi, Cori Tymoszek, Anil Jain

We present a new method of primate face recognition, and evaluate this method on several endangered primates, including golden monkeys, lemurs, and chimpanzees.

Face Recognition

On the Intrinsic Dimensionality of Image Representations

2 code implementations CVPR 2019 Sixue Gong, Vishnu Naresh Boddeti, Anil K. Jain

This paper addresses the following questions pertaining to the intrinsic dimensionality of any given image representation: (i) estimate its intrinsic dimensionality, (ii) develop a deep neural network based non-linear mapping, dubbed DeepMDS, that transforms the ambient representation to the minimal intrinsic space, and (iii) validate the veracity of the mapping through image matching in the intrinsic space.

TAR

On the Capacity of Face Representation

no code implementations29 Sep 2017 Sixue Gong, Vishnu Naresh Boddeti, Anil K. Jain

Numerical experiments on unconstrained faces (IJB-C) provides a capacity upper bound of $2. 7\times10^4$ for FaceNet and $8. 4\times10^4$ for SphereFace representation at a false acceptance rate (FAR) of 1%.

Face Recognition

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