Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.
In surveillance applications, face recognition in low quality images is an important capability. There are many works that attempt to recognize low-resolution faces @cite_0 @cite_10 . Besides low-resolution, other image quality distortions may affect performance. Karam and Zhu @cite_4 present a face recognition dataset that considers five different types of quality distortions. They however do not evaluate the performance of any models on this new dataset. @cite_6 present an approach based on sparse representations that achieves good performance on this dataset.
@cite_0 @cite_10 @cite_6 @cite_4
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Due to large distances between surveillance cameras and subjects, the captured images usually have low resolution in addition to uncontrolled poses and illumination conditions that adversely affect the performance of face recognition algorithms. In this paper, we present a low-resolution face recognition technique based on Discriminant Correlation Analysis (DCA). DCA analyzes the correlation of the features in high-resolution and low-resolution images and aims to find projections that maximize the pair-wise correlations between the two feature sets and at the same time, separate the classes within each set. This makes it possible to project the features extracted from high-resolution and low-resolution images into a common space, in which we can apply matching. The proposed method is computationally efficient and can be applied to challenging real-time applications such as recognition of several faces appearing in a crowded frame of a surveillance video. Extensive experiments performed on low-resolution surveillance images from the SCface database as well as FRGC database demonstrated the efficacy of our proposed approach in the recognition of low-resolution face images, which outperformed other state-of-the-art techniques. Face recognition performance degrades considerably when the input images are of poor resolution as is often the case for images taken by surveillance cameras or from a large distance. In this paper, we propose a novel approach for the recognition of low resolution images using multidimensional scaling. From a resolution point of view, the scenario yielding the best performance is when both the probe and gallery images are of high enough resolution to discriminate across different subjects. The proposed method embeds the low resolution images in an Euclidean space such that the distances between them in the transformed space approximates the best distances had both the images been of high resolution. The mapping is learned from high resolution training images and their corresponding low resolution images using iterative majorization algorithm. Extensive evaluation of the proposed approach on different datasets like PIE and FRGC with resolution as low as 7 × 6 pixels illustrates the usefulness of the method. We show that the proposed approach significantly improves the matching performance as compared to performing standard matching in the low-resolution domain. Performance comparison with different super-resolution techniques which obtains higher-resolution images prior to recognition further signifies the effectiveness of our approach. While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions. This is common in surveillance environments where a gallery of high resolution mugshots is compared to low resolution CCTV probe images, or where the size of a given image is not a reliable indicator of the underlying resolution (e.g. poor optics). To alleviate this degradation, we propose a compensation framework which dynamically chooses the most appropriate face recognition system for a given pair of image resolutions. This framework applies a novel resolution detection method which does not rely on the size of the input images, but instead exploits the sensitivity of local features to resolution using a probabilistic multi-region histogram approach. Experiments on a resolution-modified version of the "Labeled Faces in the Wild" dataset show that the proposed resolution detector frontend obtains a 99 average accuracy in selecting the most appropriate face recognition system, resulting in higher overall face discrimination accuracy (across several resolutions) compared to the individual baseline face recognition systems. Face recognition performance degrades considerably when the input images are of Low Resolution (LR), as is often the case for images taken by surveillance cameras or from a large distance. In this paper, we propose a novel approach for matching low-resolution probe images with higher resolution gallery images, which are often available during enrollment, using Multidimensional Scaling (MDS). The ideal scenario is when both the probe and gallery images are of high enough resolution to discriminate across different subjects. The proposed method simultaneously embeds the low-resolution probe images and the high-resolution gallery images in a common space such that the distance between them in the transformed space approximates the distance had both the images been of high resolution. The two mappings are learned simultaneously from high-resolution training images using an iterative majorization algorithm. Extensive evaluation of the proposed approach on the Multi-PIE data set with probe image resolution as low as 8 &#x00D7; 6 pixels illustrates the usefulness of the method. We show that the proposed approach improves the matching performance significantly as compared to performing matching in the low-resolution domain or using super-resolution techniques to obtain a higher resolution test image prior to recognition. Experiments on low-resolution surveillance images from the Surveillance Cameras Face Database further highlight the effectiveness of the approach.