A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions

Al-Baddai, Saad; Martí-Puig, Pere; Gallego-Jutglà, Esteve; Al-Subari, Karema; Tomé, Ana Maria; Ludwig, Bernd; Lang, Elmar; Solé-Casals, Jordi

doi:10.1007/s00500-019-04150-9

Cited by 6 publications

(8 citation statements)

References 46 publications

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“…The state-of-the-art BEMD variant, Green's function in tension BEMD (GiT-BEMD) [22], which replaces the direct spline interpolation step by an interpolation based on Green's function [27], turned out to be very efficient in practical applications such as image analysis [28,29]. Interpolation using Green's function implies that the points of the interpolating envelope surface can be expressed as…”

Section: Multidimensional Emdmentioning

confidence: 99%

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Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization

Zhang¹,

Feng²,

Martí-Puig³

et al. 2021

Preprint

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Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase in amount of data puts forward higher requirements for the capability of real-time signal analysis, it is difficult for existing EMD and its variants to trade off the growth of data dimension and the speed of signal analysis. In order to decompose multi-dimensional signals at a faster speed, we present a novel signal-serialization method (serial-EMD), which concatenates multi-variate or multi-dimensional signals into a one-dimensional signal and uses various one-dimensional EMD algorithms to decompose it. To verify the effects of the proposed method, synthetic multi-variate time series, artificial 2D images with various textures and realworld facial images are tested. Compared with existing multi-EMD algorithms, the decomposition time becomes significantly reduced. In addition, the results of facial recognition with Intrinsic Mode Functions (IMFs) extracted using our method can achieve a higher accuracy than those obtained by existing multi-EMD algorithms, which demonstrates the superior performance of our method in terms of the quality of IMFs. Furthermore, this method can provide a new perspective to optimize the existing EMD algorithms, that is, transforming the structure of the input signal rather than being constrained by developing envelope computation techniques or signal decomposition methods. In summary, the study suggests that the serial-EMD technique is a highly competitive and fast alternative for multi-dimensional signal analysis.

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Section: Multidimensional Emdmentioning

confidence: 99%

“…To demonstrate the validity of the serialization method, real-world face images as the ones used with GiT-BEMD in [28] were considered. The face database (AT & T database) contains ten different images for each of the forty subjects in the database, which represents a total of 400 different images.…”

Section: Face Databasementioning

confidence: 99%

Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization

Zhang¹,

Feng²,

Martí-Puig³

et al. 2021

Preprint

Self Cite

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“…To overcome that issue, a denoising technique based on an empirical mode decomposition with Green's functions was proposed in [55]. The system uses the capability of the bi-dimensional EMD decomposition to capture (almost all of) the noise in the first IMFs.…”

Section: Classification Of Noisy Facesmentioning

confidence: 99%

“…The method used in [55] is depicted in Figure 5, in which we can see that the image is decomposed by means of the GiT-BEMD algorithm, the first IMF is discarded, the image is later reconstructed without the contribution of the noise and used to feed a classifier.…”

Section: Classification Of Noisy Facesmentioning

confidence: 99%

Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

et al. 2020

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In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.

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“…Empirical mode decomposition was originally introduced for the adaptive analysis of nonstationary and nonlinear time-domain signals and has become one of the most powerful tools for analyzing time-frequency (T-F) signal [45]. Then, EMD was extended to handle multidimensional data and acquired successful application in image tasks [46][47][48][49]. For image analysis, EMD is a fully dataadaptive multiresolution data analysis technique to decompose the multispatial resolution spatial-frequencyamplitude components of the image into a set of intrinsic mode functions (IMFs) [50,51].…”

Section: Processing 2d Medical Image With Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

Zhang

Zhi

Tao

2020

BioMed Research International

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Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.

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A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions

Cited by 6 publications

References 46 publications

Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization

Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization

Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

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