50,319 research outputs found
Enhancing face recognition at a distance using super resolution
The characteristics of surveillance video generally include low-resolution images and blurred images. Decreases in image resolution lead to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. Dictionary based super resolution pre-processing techniques have been developed to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process, being ill-posed, and results in visual artifacts that can be visually distracting to humans and/or affect machine feature extraction and face recognition algorithms. In this paper, we investigate the impact of two existing super-resolution methods to reconstruct a high resolution from single/multiple low-resolution images on face recognition. We propose an alternative scheme that is based on dictionaries in high frequency wavelet subbands. The performance of the proposed method will be evaluated on databases of high and low-resolution images captured under different illumination conditions and at different distances. We shall demonstrate that the proposed approach at level 3 DWT decomposition has superior performance in comparison to the other super resolution methods
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations
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