Gabor filters (GFs) play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved GFs that locally adapt their shape to the direction of flow. These curved GFs enable the choice of filter parameters that increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved GFs are applied to the curved ridge and valley structures of low-quality fingerprint images. First, we combine two orientation-field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation. Subsequently, these curved regions are used for estimating the local ridge frequency. Finally, curved GFs are defined based on curved regions, and they apply the previously estimated orientations and ridge frequencies for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison with state-of-the-art enhancement methods.
For low quality images, there is a substantial risk that an image enhancement step may impair the recognition performance as shown in @cite_12 (results are cited in Table of Section ). The situation is even worse for very low quality images, and current approaches focus on minimizing the efforts required by a human expert for manually marking information in images of latent prints (see @cite_6 and @cite_20 ).
@cite_20 @cite_6 @cite_12
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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. 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. 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. The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns on the implication on the society. At best, this leads to a loss of trust in digital content, but it might even cause further harm by spreading false information and the creation of fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them - either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on DeepFakes, Face2Face, and FaceSwap as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available and contains a hidden test set as well as a database of about 1.5 million manipulated images. Thus, this database is at least an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data, we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression and clearly outperforms human observers.