An efficient illumination invariant face recognition framework via illumination enhancement and DD-DTWT filtering

Baradarani, Aryaz; Wu, Qi; Ahmadi, Majid

doi:10.1016/j.patcog.2012.06.007

Cited by 80 publications

(34 citation statements)

References 33 publications

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“…In spite of its good performance, its iterative optimization proved to be very time-consuming. Recently, by using a double-density dual-tree complex wavelet transform method (DD-DTCWT), Baradarani et al decomposed an image into frequency subbands to estimate the illumination component ( Baradarani et al, 2013 ). Unlike when using other methods, the amount of reflectance is directly estimated in the Weber-face (WF) method ( Wang, Li, Yang, & Liao, 2011 ) by computing a ratio image between the center pixel and the difference of the center pixel from its neighboring pixels.…”

Section: Existing Models and Methodsmentioning

confidence: 99%

Multiscale morphology based illumination normalization with enhanced local textures for face recognition

Lee

2016

Expert Systems with Applications

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Section: Existing Models and Methodsmentioning

confidence: 99%

Multiscale morphology based illumination normalization with enhanced local textures for face recognition

Lee

2016

Expert Systems with Applications

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“…The multilayer learning architecture that uses ELM auto-encoder [11] and subnetwork nodes [12] expands ELM from a single layer structure to a multilayer structure. Also, recent applications of ELM have included: machine vision [13,14], ensemble learning [15,16], sparse learning [17,18] big data applications [19,20], etc.…”

Section: Smooth Average Congestedmentioning

confidence: 99%

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

2017

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Abstract:There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition.

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“…This type of methods mainly consists of multiscale retinex (MSR) [10], DCT-based nor-malization technique (DCT) [11], self-quotient image (SQI) [12], logarithmic total variation (LTV) [13,14], gabor based methods [15], wavelet-based methods [16,17], contourlet-based methods [18], Bidimensional empirical mode decomposition (BEMD) [19], Gradient [20] and Weber [21]. MSR [10] combines several low-pass filters (i.e., Gaussian filters) for illumination estimation in a logarithm domain, which can cause serious halo effect.…”

Section: Introductionmentioning

confidence: 99%

“…Furemore, illumination invariants extracted by wavelet-based [16,17] methods have obvious Gibbs phenomena and contourlet-based [18] methods tend to destory much useful information. BEMD [19] method has quite high computational expense.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Illumination-Insensitive Feathers for Face Recognition under Complex Illumination Conditions

Zhuang¹,

Guan²

2018

Preprint

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Abstract:Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this paper, we propose a novel method to improve the illumination invariants for solving this challenge. Firstly, a new method based on the Lambert reflectance model is proposed to extract illumination invariant, which is less insensitive to complex illumination variations. Secondly, in order to repair the defects caused by process of illumination invariants extraction, Fast Mean Filter is utilized to smooth and remove noise. Lastly, for raising the richness of information in output image, a nonlinear normalization transformation is proposed. Compared with the state-of-the-arts, experimental results show that the proposed method can extract more robust illumination invariants. Apart from it, the richness of information in processed image is greater and superior performance in face recognition rate is superior.

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An efficient illumination invariant face recognition framework via illumination enhancement and DD-DTWT filtering

Cited by 80 publications

References 33 publications

Multiscale morphology based illumination normalization with enhanced local textures for face recognition

Multiscale morphology based illumination normalization with enhanced local textures for face recognition

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

Improvement of Illumination-Insensitive Feathers for Face Recognition under Complex Illumination Conditions

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