2022-06-13 更新
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
Authors:Haiyu Wu, Vítor Albiero, K. S. Krishnapriya, Michael C. King, Kevin W. Bowyer
This is the first work that we are aware of to explore how the level of brightness of the skin region in a pair of face images impacts face recognition accuracy. Image pairs with both images having mean face skin brightness in an upper-middle range of brightness are found to have the highest matching accuracy across demographics and matchers. Image pairs with both images having mean face skin brightness that is too dark or too light are found to have an increased false match rate (FMR). Image pairs with strongly different face skin brightness are found to have decreased FMR and increased false non-match rate (FNMR). Using a brightness information metric that captures the variation in brightness in the face skin region, the variation in matching accuracy is shown to correlate with the level of information available in the face skin region. For operational scenarios where image acquisition is controlled, we propose acquiring images with lighting adjusted to yield face skin brightness in a narrow range.
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CORE: Consistent Representation Learning for Face Forgery Detection
Authors:Yunsheng Ni, Depu Meng, Changqian Yu, Chengbin Quan, Dongchun Ren, Youjian Zhao
Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue. To relieve this issue, there is a trend to introduce some erasing-based augmentations. We find that these methods indeed attempt to implicitly induce more consistent representations for different augmentations via assigning the same label for different augmented images. However, due to the lack of explicit regularization, the consistency between different representations is less satisfactory. Therefore, we constrain the consistency of different representations explicitly and propose a simple yet effective framework, COnsistent REpresentation Learning (CORE). Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency. Extensive experiments (in-dataset and cross-dataset) demonstrate that CORE performs favorably against state-of-the-art face forgery detection methods.
PDF Accepted by CVPRW 2022
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Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement
Authors:Ziming Yang, Jian Liang, Chaoyou Fu, Mandi Luo, Xiao-Yu Zhang
Heterogeneous Face Recognition (HFR) aims to match faces across different domains (e.g., visible to near-infrared images), which has been widely applied in authentication and forensics scenarios. However, HFR is a challenging problem because of the large cross-domain discrepancy, limited heterogeneous data pairs, and large variation of facial attributes. To address these challenges, we propose a new HFR method from the perspective of heterogeneous data augmentation, named Face Synthesis with Identity-Attribute Disentanglement (FSIAD). Firstly, the identity-attribute disentanglement (IAD) decouples face images into identity-related representations and identity-unrelated representations (called attributes), and then decreases the correlation between identities and attributes. Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images. Both the original images and the synthetic ones are utilized to train the HFR network for tackling the challenges and improving the performance of HFR. Extensive experiments on five HFR databases validate that FSIAD obtains superior performance than previous HFR approaches. Particularly, FSIAD obtains 4.8% improvement over state of the art in terms of VR@FAR=0.01% on LAMP-HQ, the largest HFR database so far.
PDF Accepted for publication in IEEE Transactions on Information Forensics and Security (TIFS)
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