Adaptive Deep Iris Feature Extractor at Arbitrary Resolutions
This addresses a domain-specific problem in biometric security by improving iris recognition robustness across varying image resolutions, though it is incremental as it builds on existing neural network models.
The paper tackles the problem of iris recognition performance degradation at arbitrary resolutions by proposing a resolution-adaptive feature extractor with automatically switching networks, achieving enhanced recognition at low resolutions while maintaining high-resolution performance.
This paper proposes a deep feature extractor for iris recognition at arbitrary resolutions. Resolution degradation reduces the recognition performance of deep learning models trained by high-resolution images. Using various-resolution images for training can improve the model's robustness while sacrificing recognition performance for high-resolution images. To achieve higher recognition performance at various resolutions, we propose a method of resolution-adaptive feature extraction with automatically switching networks. Our framework includes resolution expert modules specialized for different resolution degradations, including down-sampling and out-of-focus blurring. The framework automatically switches them depending on the degradation condition of an input image. Lower-resolution experts are trained by knowledge-distillation from the high-resolution expert in such a manner that both experts can extract common identity features. We applied our framework to three conventional neural network models. The experimental results show that our method enhances the recognition performance at low-resolution in the conventional methods and also maintains their performance at high-resolution.