Iris recognition based on bidimensional empirical mode decomposition and fractal dimension

Chen, Wei-Kuei; Lee, Jen-Chun; Han, Weiyu; Shih, Chih-Kuang; Chang, Ko-Chin

doi:10.1016/j.ins.2012.09.021

Cited by 42 publications

(16 citation statements)

References 32 publications

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“…It is used for analyzing heart beats or EEG signals while a subject is performing a particular cognitive task [23].It is applied to analyze wave signals propagating through structures such as vibration and detect anomalies such as cracks [18]. Image processing is another area of application in which it was used for filtering and feature detection [24].On a more macro scale, it has been applied to extracting periodic modes of epidemic outbreak time-series, and analyzing time-series data from weekly mortgage rates [25,26]. Another area where HHT has been extensively used is structural engineering [18].…”

Section: Fast Fourier Transform (Fft) and Waveletmentioning

confidence: 99%

Applying Hilbert-Huang Transform to Extract Essential Patterns from Hand Accelerometer Data

Choe¹,

Suh²

2017

JIIBC

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Hand Accelerometers are widely used to detect human motion patterns in real-time. It is essential to reliably identify which type of activity is performed by human subjects. This rests on having accurate template of each activity. Many human activities are represented as a set of multiple time-series data from such sensors, which are mostly non-stationary and non-linear in nature. This requires a method which can effectively extract patterns from non-stationary and non-linear data. To achieve such a goal, we propose the method to apply Hilbert-Huang Transform which is known to be an effective way of extracting non-stationary and non-linear components from time-series data. It is applied on samples of accelerometer data to determine its effectiveness.

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Section: Fast Fourier Transform (Fft) and Waveletmentioning

confidence: 99%

Applying Hilbert-Huang Transform to Extract Essential Patterns from Hand Accelerometer Data

Choe¹,

Suh²

2017

JIIBC

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“…Chen, Wei-Kuei, et al, [18] has attempted to detect shape information from the iris by analyzing local intensity variations of an iris image. The methodology involves extraction of iris features using bi-dimensional empirical mode decomposition (BEMD) and fractal dimension.…”

Section: Literature Surveymentioning

confidence: 99%

Two Levels Fusion Based Multimodal Biometric Authentication Using Iris and Fingerprint Modalities

Sireesha¹,

Rani²,

Reddy³

et al. 2016

IJIES

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Abstract:In the document, we are intending to present an innovative technique for the multimodal biometric authentication. Initially the input image is preprocessed then offered to feature extraction, where the modified local binary pattern is effectively utilized. Thereafter, the extracted features are furnished to the feature level and score level fusions. In feature level fusion, extracted features are offered to the GSO where the optimal features are shortlisted, and are furnished to the optimized neural network which effectively detects the iris and fingerprint image. In score level fusion, extracted features from the iris image are offered to the PSO and naive bayes classifier here one score value is achieved. After that, extracted features from the fingerprint image are applied to the AGFS and then one score value is attained. Finally, both the score values are combined. The evaluation tools utilized precision, FAR and FRR. The proposed method implemented in MATLAB platform.

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“…The fractal analysis has been extensively used for the image analysis [21], feature extraction and matching [22,23], pattern recognition [24], computer vision [25], and composite-material 3D reconstruction [26]. Its main characteristics are self-similarity and the calibration invariance.…”

Section: Introductionmentioning

confidence: 99%

Abdominal-Deformation Measurement for a Shape-Flexible Mannequin Using the 3D Digital Image Correlation

Huan¹,

Hao²,

Ding³

2017

Journal of Computing Science and Engineering

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In this paper, the abdominal-deformation measurement scheme is conducted on a shape-flexible mannequin using the DIC technique in a stereo-vision system. Firstly, during the integer-pixel displacement search, a novel fractal dimension based on an adaptive-ellipse subset area is developed to track an integer pixel between the reference and deformed images. Secondly, at the subpixel registration, a new mutual-learning adaptive particle swarm optimization (MLADPSO) algorithm is employed to locate the subpixel precisely. Dynamic adjustments of the particle flight velocities that are according to the deformation extent of each interest point are utilized for enhancing the accuracy of the subpixel registration. A test is performed on the abdominal-deformation measurement of the shape-flexible mannequin. The experiment results indicate that under the guarantee of its measurement accuracy without the cause of any loss, the time-consumption of the proposed scheme is significantly more efficient than that of the conventional method, particularly in the case of a large number of interest points.

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Iris recognition based on bidimensional empirical mode decomposition and fractal dimension

Cited by 42 publications

References 32 publications

Applying Hilbert-Huang Transform to Extract Essential Patterns from Hand Accelerometer Data

Applying Hilbert-Huang Transform to Extract Essential Patterns from Hand Accelerometer Data

Two Levels Fusion Based Multimodal Biometric Authentication Using Iris and Fingerprint Modalities

Abdominal-Deformation Measurement for a Shape-Flexible Mannequin Using the 3D Digital Image Correlation

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