Face and palmprint multimodal biometric systems using Gabor–Wigner transform as feature extraction

Saini, Nirmala; Sinha, Aloka

doi:10.1007/s10044-014-0414-6

Cited by 25 publications

(16 citation statements)

References 47 publications

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“…The Sigmoid function is widely used as a common nonlinear transform [ 23 , 24 , 25 ]. Its definition is shown in Equation (15):

where

is the inclined coefficient to adjust

at different scales.…”

Section: The Tuneable Sigmoid-based Fractional Power Spectrum Densmentioning

confidence: 99%

A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments

Younan

Shi

2018

Sensors

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In this paper, a novel method, that employs a fractional Fourier transform and a tuneable Sigmoid transform, is proposed, in order to estimate the Doppler stretch and time delay of wideband echoes for a linear frequency modulation (LFM) pulse radar in an alpha-stable distribution noise environment. Two novel functions, a tuneable Sigmoid fractional correlation function (TS-FC) and a tuneable Sigmoid fractional power spectrum density (TS-FPSD), are presented in this paper. The novel algorithm based on the TS-FPSD is then proposed to estimate the Doppler stretch and the time delay. Then, the derivation of unbiasedness and consistency is presented. Furthermore, the boundness of the TS-FPSD to the symmetric alpha stable (SαS) noise, the parameter selection of the TS-FPSD, and the feasibility analysis of the TS-FPSD, are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation is derived and computed in closed form, which shows that better performance has been achieved. Simulation results and theoretical analysis are presented, to demonstrate the applicability of the forgoing method. It is shown that the proposed method can not only effectively suppress impulsive noise interference, but it also does not need a priori knowledge of the noise with higher estimation accuracy in alpha-stable distribution noise environments.

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“…The Sigmoid function is widely used as a common nonlinear transform [ 23 , 24 , 25 ]. Its definition is shown in Equation (15):

where

is the inclined coefficient to adjust

at different scales.…”

Section: The Tuneable Sigmoid-based Fractional Power Spectrum Densmentioning

confidence: 99%

A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments

Younan

Shi

2018

Sensors

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show abstract

“…Sigmoid transform is a commonly used nonlinear transform [12][13][14]. Its definition is shown in Eq.…”

Section: Sigmoidccamentioning

confidence: 99%

A Robust Joint Estimation Method of Time Delay and Doppler Frequency Shift

Yu¹,

Niu²,

Zhang³

2018

dtcse

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Abstract. In this paper, a novel concept, the Sigmoid transform based cyclic correlation, is proposed and a relative estimator named the Sigmoid transform based cyclic cross-ambiguity function is also derived to handle the joint estimation problem in the presence of impulsive noise and co-channel interference. Based on these definitions, a novel joint estimation method of the time delay and the Doppler frequency shift is proposed. Simulations have verified its superior performances over existing methods based on cyclic ambiguity function or generalized fractional lower-order cyclic cross-ambiguity function, especially under impulsive noise. Meanwhile, it is more applicable for practical applications since it is parameter-free.

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“…Variety of methods exist for the conversion of spatial domain signal to spatial frequency domain. These methods include Fourier Transform [5], Wavelet Transform [6], and Wigner distribution [7]. However, these methods are not adaptive.…”

Section: Introductionmentioning

confidence: 99%

Spatial frequency based video stream analysis for object classification and recognition in clouds

Yaseen

Anjum

Antonopoulos

2016

Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to extract useful information. However, the video data obtained by these cheap cameras is often of low quality and results in blur video content. Moreover, various illumination effects caused by lightning conditions also degrade the video quality. These effects present severe challenges for object classification. We present a cloud-based blur and illumination invariant approach for object classification from images and video data. The bi-dimensional empirical mode decomposition (BEMD) has been adopted to decompose a video frame into intrinsic mode functions (IMFs). These IMFs further undergo to first order Reisz transform to generate monogenic video frames. The analysis of each IMF has been carried out by observing its local properties (amplitude, phase and orientation) generated from each monogenic video frame. We propose a stack based hierarchy of local pattern features generated from the amplitudes of each IMF which results in blur and illumination invariant object classification. The extensive experimentation on video streams as well as publically available image datasets reveals that our system achieves high accuracy from 0.97 to 0.91 for increasing Gaussian blur ranging from 0.5 to 5 and outperforms state of the art techniques under uncontrolled conditions. The system also proved to be scalable with high throughput when tested on a number of video streams using cloud infrastructure.

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Face and palmprint multimodal biometric systems using Gabor–Wigner transform as feature extraction

Cited by 25 publications

References 47 publications

A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments

A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments

A Robust Joint Estimation Method of Time Delay and Doppler Frequency Shift

Spatial frequency based video stream analysis for object classification and recognition in clouds

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