Optimizing a Homomorphic Filter for Illumination Compensation In Face Recognition Using Population-Based Algorithms

Plichoski, Guilherme Felippe; Chidambaram, Chidambaram; Parpinelli, Rafael Stubs

doi:10.1109/wvc.2017.00021

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…For the TM task, the individual is represented as a four-dimensional vector containing horizontal and vertical coordinates, scale factor and rotation angle. All works used the Euclidean distance measure to drive the search and to calculate the distance between the images (Chidambaram et al;. performed by each author usually di ers, which sometimes makes it unreliable, di cult to compare with others and to replicate results.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Many authors employed bio-inspired algorithms to optimize the intrinsic parameters in their proposed methodologies, such as selecting the parameters G and C of Support Vector Machine (SVM) classi cation (Valuvanathorn et al;, searching the optimum Hidden Markov Model (HMM) states and parameters (Farag et al;, or adjusting the parameters of a homomorphic lter (Plichoski et al;. In addition to these, there are other bio-inspired approaches that are employed for template matching, which consists in nding areas of an image that better match to a template image (Chidambaram et al;. As preprocessing step, template matching might deal with problems such as scale and rotation variations.…”

Section: Face Recognitionmentioning

confidence: 99%

“…The classi cation rate is used as the tness value. Finally, Plichoski et al (2017) proposed to use PSO to optimize Homomorphic lter (HF) parameters namely high and low frequency factors, cut-o frequency and lter's order. The recognition rate is used as tness function to guide the algorithms search.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…After ltering the results through objective, inclusion and exclusion criteria, seventy three relevant works are gathered for SLR purpose. Some of the bio-inspired algorithms used in 2D FR systems for di erent purposes are template matching (Chidambaram et al;, classi cation (Nebti and Boukerram;, parameters optimization Shen et al;Fernández-Martínez and Cernea;Loderer and Pavlovičová;, and feature selection (Khadhraoui et al;Farag et al;.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

Plichoski

Chidambaram

Parpinelli

2018

RBCA

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A wide range of approaches for 2D face recognition (FR) systems can be found in the literature due to its high applicability and issues that need more investigation yet which include occlusion, variations in scale, facial expression, and illumination. Over the last years, a growing number of improved 2D FR systems using Swarm Intelligence and Evolutionary Computing algorithms have emerged. The present work brings an up-to-date Systematic Literature Review (SLR) concerning the use of Swarm Intelligence and Evolutionary Computation applied in 2D FR systems. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research.Key words: Bio-inspired algorithms; Evolutionary algorithms; Face Recognition; Natural Computing; Optimization; Swarm algorithms ResumoUma ampla gama de abordagens para sistemas de reconhecimento facial (FR) 2D pode ser encontrada na literatura devido a sua alta aplicabilidade e também por questões que necessitam de mais investigação, incluindo oclusão, variações de escala, expressão facial e iluminação. Nos últimos anos, um número crescente de sistemas de FR 2D usando algoritmos de Inteligência de Enxame e Computação Evolucionária surgiram. O presente trabalho traz uma Revisão Sistemática de Literatura (SLR) atualizada sobre o uso da Inteligência de Enxame e Computação Evolucionária aplicada em sistemas de FR 2D. Além disso, esta revisão analisa e aponta as principais técnicas e algoritmos utilizados e sugere alguns direcionamentos para pesquisas futuras.

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Face Recognitionmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

Plichoski

Chidambaram

Parpinelli

2018

RBCA

Self Cite

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“…In the spatial domain, image enhancement synchronously changes the two components, and hence, these algorithms may not complete the requested performance due to strengthening the irradiance. Moreover, in the frequency domain, Fourier transform (FT)-based frequency component has been used to represent the irradiance and reflectance in [ 53 , 54 , 55 , 56 ]. In addition, the irradiance and reflectance are related to low-frequency and high-frequency components in the FT-based frequency spectrum, respectively [ 19 , 20 ].…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Liu

Jia

et al. 2020

Sensors

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Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The quality of image matching is heavily dependent on image details and naturalness. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.

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Image enhancement method for visual localization and mapping in complex light scenarios

Jia

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Visual localization and mapping have received considerable attention in the fields of computer vision, photogrammetry, and remote sensing. Image matching is key to visual localization and mapping. However, light is complex and uneven in real scenarios, creating difficulties in feature extraction and matching. Hence, feature mismatch and loss reduce the efficiency, accuracy, and robustness of visual localization and mapping. We developed a visual localization and mapping method for complex light scenarios based on image enhancement. Starting with initial images, the irradiance and reflectance components were separated based on logarithmic transformation. Our method strengthened high-frequency components and restrained low-frequency components with improved homomorphic filtering, restraining the light component and enhancing the important reflection component. The SIFT algorithm was used for feature detection and matching. The proposed method was tested on images with uneven light captured using a stereo vision camera in an indoor environment, focusing on visual localization and mapping. The experimental results emphasized that the method improved the rate of image localization and number of point clouds, as well as the reprojection error, which ranged from 0.85 to 0.82 on average. Thus, the proposed method is robust rather than probabilistic for improving visual localization and mapping under complex light conditions.

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Optimizing a Homomorphic Filter for Illumination Compensation In Face Recognition Using Population-Based Algorithms

Cited by 3 publications

References 15 publications

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Image enhancement method for visual localization and mapping in complex light scenarios

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