Face Recognition Using Sparse Fingerprint Classification Algorithm

Larrain, Tomás Vivanco; Bernhard, John S.; Mery, Domingo; Bowyer, Kevin W.

doi:10.1109/tifs.2017.2680403

Cited by 32 publications

(8 citation statements)

References 47 publications

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“…Note, that the original dimensionality, m of the four (n=4) data points illustrated in Fig.1 does not matter and the visualisation is always 2D. This characteristic of the proposed DA plane can be very useful for high dimensional problems such as NLP [3], [38], [39], genome decoding [43], spectroscopy [19], fault detection of aviation data [32], image recognition [27], [30], etc.…”

Section: Distance/dissimilarity Components In Sodamentioning

confidence: 97%

Self-Organised direction aware data partitioning algorithm

Angelov

Kangin

et al. 2018

Information Sciences

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In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware (SODA) data partitioning and forming data clouds is proposed. The proposed SODA algorithm employs an extra cosine similarity-based directional component to work together with a traditional distance metric, thus, takes the advantages of both the spatial and angular divergences. Using the nonparametric Empirical Data Analytics (EDA) operators, the proposed algorithm automatically identifies the main modes of the data pattern from the empirically observed data samples and uses them as focal points to form data clouds. A streaming data processing extension of the SODA algorithm is also proposed. This extension of the SODA algorithm is able to self-adjust the data clouds structure and parameters to follow the possibly changing data patterns and processes.Numerical examples provided as a proof of the concept illustrate the proposed algorithm as an autonomous algorithm and demonstrate its high clustering performance and computational efficiency.

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Section: Distance/dissimilarity Components In Sodamentioning

confidence: 97%

Self-Organised direction aware data partitioning algorithm

Angelov

Kangin

et al. 2018

Information Sciences

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“…The tanh function is slightly different from the sigmoid activation function because it keeps fitting the input value in the range of −1 and 1 as shown in Equation (2).…”

Section: Iris Segmentation By Cnnmentioning

confidence: 99%

“…Behavioral biometrics considers voice, signature, keystroke, and gait recognition [1], whereas physiological biometrics considers face [2,3], iris [4,5], fingerprints [6], finger vein patterns [7], and palm prints [8]. Iris recognition has been widely used in security and authentication systems because of its reliability and high-security [9,10].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment

et al. 2017

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Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR) light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN); which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II) training database (selected from the UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE) dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.

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“…Till now, various classification algorithms have been successfully developed and widely used in different areas i.e. remote sensing [46], [47], face recognition [10], [25], handwritten digits recognition [13], [21], etc.…”

Section: Introductionmentioning

confidence: 99%

Self-organising fuzzy logic classifier

Angelov

2018

Information Sciences

114

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In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming data afterwards to follow the changing data pattern by updating the system structure and meta-parameters recursively. The meta-parameters of the proposed approach are derived from data directly. By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs. The classifier also supports different types of distance measures. Numerical examples based on benchmark datasets demonstrate the high performance of the proposed approach and its ability of handling highdimensional, complex, large-scale problems.

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Face Recognition Using Sparse Fingerprint Classification Algorithm

Cited by 32 publications

References 47 publications

Self-Organised direction aware data partitioning algorithm

Self-Organised direction aware data partitioning algorithm

Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment

Self-organising fuzzy logic classifier

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