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- Alonso-Fernandez, Fernando, 1978-, et al.
(author)
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A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
- 2019
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In: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 7, s. 6519-6544
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Journal article (peer-reviewed)abstract
- The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches need thus to incorporate specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an Eigen-patches reconstruction method based on PCA Eigentransformation of local image patches. The structure of the iris is exploited by building a patch-position dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is among the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators, which were used to carry out biometric verification and identification experiments. Experimental results show that the proposed method significantly outperforms both bilinear and bicubic interpolation at very low-resolution. The performance of a number of comparators attain an impressive Equal Error Rate as low as 5%, and a Top-1 accuracy of 77-84% when considering iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching. © 2018, Emerald Publishing Limited.
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| 2. |
- Gonzalez-Sosa, Ester, et al.
(author)
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Exploring Body Texture From mmW Images for Person Recognition
- 2019
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In: IEEE Transactions on Biometrics, Behavior, and Identity Science. - Piscataway, NJ : IEEE. - 2637-6407. ; 1:2, s. 139-151
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Journal article (peer-reviewed)abstract
- Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants, such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this paper we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained convolutional neural networks (CNNs) models. First, we analyze the individual performance of three mmW body parts, concluding that: 1) mmW torso region is more discriminative than mmW face and the whole body; 2) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body; and 3) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this paper, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported.
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| 3. |
- Gonzalez-Sosa, Ester, et al.
(author)
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Facial Soft Biometrics for Recognition in the Wild : Recent Works, Annotation and Evaluation
- 2018
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In: IEEE Transactions on Information Forensics and Security. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 1556-6013 .- 1556-6021. ; 13:8, s. 2001-2014
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Journal article (peer-reviewed)abstract
- The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores. © 2018 IEEE.
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