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| 2. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
- 2019
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Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 7, s. 6519-6544
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Tidskriftsartikel (refereegranskat)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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| 3. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Fingerprint Databases and Evaluation
- 2015. - 2
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Ingår i: Encyclopedia of Biometrics. - New York : Springer Science+Business Media B.V.. - 9781489974877 - 9781489974884 ; , s. 599-606
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Bokkapitel (refereegranskat)abstract
- This is an excerpt from the contentSynonymsFingerprint benchmark; Fingerprint corpora; Fingerprint datasetDefinitionFingerprint databases are structured collections of fingerprint data mainly used for either evaluation or operational recognition purposes.Fingerprint data in databases for evaluation are usually detached from the identity of corresponding individuals. These databases are publicly available for research purposes, and they usually consist of raw fingerprint images acquired with live-scan sensors or digitized from inked fingerprint impressions on paper. Databases for evaluation are the basis for research in automatic fingerprint recognition, and together with specific experimental protocols, they are the basis for a number of technology evaluations and benchmarks. This is the type of fingerprint databases further covered here.On the other hand, fingerprint databases for operational recognition are typically proprietary, they usually incorporate personal information about the enrolled people together with the fingerprint data, and they can incorporate either raw fingerprint image data or some form of distinctive fingerprint descriptors such as minutiae templates. These fingerprint databases represent one of the modules in operational automated fingerprint recognition systems, and they will not be adressed here.
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| 4. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Quality Measures in Biometric Systems
- 2015. - 2
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Ingår i: Encyclopedia of Biometrics. - New York : Springer Science+Business Media B.V.. - 9781489974877 - 9781489974884 ; , s. 1287-1297
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Bokkapitel (refereegranskat)abstract
- This is an excerpt from the contentSynonymsQuality assessment; Biometric quality; Quality-based processingDefinitionSince the establishment of biometrics as a specific research area in the late 1990s, the biometric community has focused its efforts in the development of accurate recognition algorithms [1]. Nowadays, biometric recognition is a mature technology that is used in many applications, offering greater security and convenience than traditional methods of personal recognition [2].During the past few years, biometric quality measurement has become an important concern after a number of studies and technology benchmarks that demonstrate how performance of biometric systems is heavily affected by the quality of biometric signals [3]. This operationally important step has been nevertheless under-researched compared to the primary feature extraction and pattern recognition tasks [4]. One of the main challenges facing biometric technologies is performance degradation in less controlled situations, and the problem of biometric quality measurement has arisen even stronger with the proliferation of portable handheld devices, with at-a-distance and on-the-move acquisition capabilities. These will require robust algorithms capable of handling a range of changing characteristics [2]. Another important example is forensics, in which intrinsic operational factors further degrade recognition performance.There are number of factors that can affect the quality of biometric signals, and there are numerous roles of a quality measure in the context of biometric systems. This section summarizes the state of the art in the biometric quality problem, giving an overall framework of the different challenges involved.
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| 5. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Super-Resolution for Selfie Biometrics : Introduction and Application to Face and Iris
- 2019. - 1
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Ingår i: Selfie Biometrics. - Cham : Springer. - 9783030269715 - 9783030269722 ; , s. 105-128
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Bokkapitel (refereegranskat)abstract
- Biometric research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severly affecting the accuracy of recognition systems if not tackled appropriately. In this chapter, we give an overview of recent research in super-resolution reconstruction applied to biometrics, with a focus on face and iris images in the visible spectrum, two prevalent modalities in selfie biometrics. After an introduction to the generic topic of super-resolution, we investigate methods adapted to cater for the particularities of these two modalities. By experiments, we show the benefits of incorporating super-resolution to improve the quality of biometric images prior to recognition. © Springer Nature AG 2019
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| 6. |
- Gonzalez-Sosa, Ester, et al.
(författare)
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Exploring Body Texture From mmW Images for Person Recognition
- 2019
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Ingår i: IEEE Transactions on Biometrics, Behavior, and Identity Science. - Piscataway, NJ : IEEE. - 2637-6407. ; 1:2, s. 139-151
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Tidskriftsartikel (refereegranskat)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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| 7. |
- Gonzalez-Sosa, Ester, et al.
(författare)
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Facial Soft Biometrics for Recognition in the Wild : Recent Works, Annotation and Evaluation
- 2018
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Ingår i: 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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Tidskriftsartikel (refereegranskat)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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| 8. |
- Krish, Ram P., et al.
(författare)
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Improving Automated Latent Fingerprint Identification Using Extended Minutia Types
- 2019
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Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 50, s. 9-19
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Tidskriftsartikel (refereegranskat)abstract
- Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features. © 2018 Elsevier B.V.
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| 9. |
- Krish, Ram Prasad, et al.
(författare)
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Pre-registration of latent fingerprints based on orientation field
- 2015
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Ingår i: IET Biometrics. - Stevenage : Institution of Engineering and Technology. - 2047-4938 .- 2047-4946. ; 4:2, s. 42-52
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Tidskriftsartikel (refereegranskat)abstract
- In this study, the authors present a hierarchical algorithm to register a partial fingerprint against a full fingerprint using only the orientation fields. In the first level, they shortlist possible locations for registering the partial fingerprint in the full fingerprint using a normalised correlation measure, taking various rotations into account. As a second level, on those candidate locations, they calculate three other similarity measures. They then perform score fusion for all the estimated similarity scores to locate the final registration. By registering a partial fingerprint against a full fingerprint, they can reduce the search space of the minutiae set in the full fingerprint, thereby improving the result of partial fingerprint identification, particularly for poor quality latent fingerprints. They report the rank identification improvements of two minutiae-based automated fingerprint identification systems on the National Institute of Standards and Technology (NIST)-Special Database 27 database when they use the authors hierarchical registration as a pre-alignment. © The Institution of Engineering and Technology 2015.
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