215,291 research outputs found

    An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics

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    Biometric systems have to address many requirements, such as large population coverage, demographic diversity, varied deployment environment, as well as practical aspects like performance and spoofing attacks. Traditional unimodal biometric systems do not fully meet the aforementioned requirements making them vulnerable and susceptible to different types of attacks. In response to that, modern biometric systems combine multiple biometric modalities at different fusion levels. The fused score is decisive to classify an unknown user as a genuine or impostor. In this paper, we evaluate combinations of score normalization and fusion techniques using two modalities (fingerprint and finger-vein) with the goal of identifying which one achieves better improvement rate over traditional unimodal biometric systems. The individual scores obtained from finger-veins and fingerprints are combined at score level using three score normalization techniques (min-max, z-score, hyperbolic tangent) and four score fusion approaches (minimum score, maximum score, simple sum, user weighting). The experimental results proved that the combination of hyperbolic tangent score normalization technique with the simple sum fusion approach achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201

    Hybrid Fusion for Biometrics: Combining Score-level and Decision-level Fusion

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    A general framework of fusion at decision level, which works on ROCs instead of matching scores, is investigated. Under this framework, we further propose a hybrid fusion method, which combines the score-level and decision-level fusions, taking advantage of both fusion modes. The hybrid fusion adaptively tunes itself between the two levels of fusion, and improves the final performance over the original two levels. The proposed hybrid fusion is simple and effective for combining different biometrics

    Hybrid 2D and 3D face verification

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    Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures). A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation: 1. the use of holistic difference vectors of the face, and 2. by dividing the 3D face into Free-Parts. The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined. This thesis also examines methods for performing classifier fusion score fusion. Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques. The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed. The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%

    Lumbar interspinous process fixation and fusion with stand-alone interlaminar lumbar instrumented fusion implant in patients with degenerative spondylolisthesis undergoing decompression for spinal stenosis

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    Abstract STUDY DESIGN: Prospective cohort study. PURPOSE: To assess the ability of a stand-alone lumbar interspinous implant (interspinous/interlaminar lumbar instrumented fusion, ILIF) associated with bone grafting to promote posterior spine fusion in degenerative spondylolisthesis (DS) with vertebral instability. OVERVIEW OF LITERATURE: A few studies, using bilateral laminotomy (BL) or bilateral decompression by unilateral laminotomy (BDUL), found satisfactory results in stenotic patients with decompression alone, but others reported increased olisthesis, or subsequent need for fusion in DS with or without dynamic instability. METHODS: Twenty-five patients with Grade I DS, leg pain and chronic low back pain underwent BL or BDUL and ILIF implant. Olisthesis was 13% to 21%. Follow-up evaluations were performed at 4 to 12 months up to 25 to 44 months (mean, 34.4). Outcome measures were numerical rating scale (NRS) for back and leg pain, Oswestry disability index (ODI) and short-form 36 health survey (SF-36) of body pain and function. RESULTS: Fusion occurred in 21 patients (84%). None had increased olisthesis or instability postoperatively. Four types of fusion were identified. In Type I, the posterior part of the spinous processes were fused. In Type II, fusion extended to the base of the processes. In Type III, bone was present also around the polyetheretherketone plate of ILIF. In Type IV, even the facet joints were fused. The mean NRS score for back and leg pain decreased by 64% and 80%, respectively. The mean ODI score was decreased by 52%. SF-36 bodily pain and physical function mean scores increased by 53% and 58%, respectively. Computed tomography revealed failed fusion in four patients, all of whom still had vertebral instability postoperatively. CONCLUSIONS: Stand-alone ILIF with interspinous bone grafting promotes vertebral fusion in most patients with lumbar stenosis and unstable Grade I DS undergoing BL or BDUL

    BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks

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    We present a simple and effective framework for simultaneous semantic segmentation and instance segmentation with Fully Convolutional Networks (FCNs). The method, called BiSeg, predicts instance segmentation as a posterior in Bayesian inference, where semantic segmentation is used as a prior. We extend the idea of position-sensitive score maps used in recent methods to a fusion of multiple score maps at different scales and partition modes, and adopt it as a robust likelihood for instance segmentation inference. As both Bayesian inference and map fusion are performed per pixel, BiSeg is a fully convolutional end-to-end solution that inherits all the advantages of FCNs. We demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.Comment: BMVC201
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