114 research outputs found
Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning
Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL
CCFace: Classification Consistency for Low-Resolution Face Recognition
In recent years, deep face recognition methods have demonstrated impressive
results on in-the-wild datasets. However, these methods have shown a
significant decline in performance when applied to real-world low-resolution
benchmarks like TinyFace or SCFace. To address this challenge, we propose a
novel classification consistency knowledge distillation approach that transfers
the learned classifier from a high-resolution model to a low-resolution
network. This approach helps in finding discriminative representations for
low-resolution instances. To further improve the performance, we designed a
knowledge distillation loss using the adaptive angular penalty inspired by the
success of the popular angular margin loss function. The adaptive penalty
reduces overfitting on low-resolution samples and alleviates the convergence
issue of the model integrated with data augmentation. Additionally, we utilize
an asymmetric cross-resolution learning approach based on the state-of-the-art
semi-supervised representation learning paradigm to improve discriminability on
low-resolution instances and prevent them from forming a cluster. Our proposed
method outperforms state-of-the-art approaches on low-resolution benchmarks,
with a three percent improvement on TinyFace while maintaining performance on
high-resolution benchmarks.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB
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