Face Recognition Using Block Based Feature Extraction with CZT and Goertzel-algorithm as a Preprocessing Technique

Varadarajan, K.K.; Suhasini, P.R.; Manikantan, K.; Ramachandran, S.

doi:10.1016/j.procs.2015.02.065

Cited by 12 publications

(4 citation statements)

References 12 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additive fusion block based proposed by Varadarajan et al that used block-based extraction process [40]. Block based can maintain the optimum number of features that need to be extracted.…”

Section: Additive Fusion Block Basedmentioning

confidence: 99%

Features for Cross Spectral Image Matching: A Survey

Oktiana¹,

Arnia²,

Away³

et al. 2018

Bulletin EEI

View full text Add to dashboard Cite

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.

show abstract

“…Additive fusion block based proposed by Varadarajan et al that used block-based extraction process [40]. Block based can maintain the optimum number of features that need to be extracted.…”

Section: Additive Fusion Block Basedmentioning

confidence: 99%

Features for Cross Spectral Image Matching: A Survey

Oktiana¹,

Arnia²,

Away³

et al. 2018

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Like others, Vora et al (2014) tried the FS on Gamma Ray Burst Rhombus Star (GRBRS) feature mask space and Varun et al (2015) on Block-wise Hough Transform (HT) feature space. The nal work which used the distance between class as a tness evaluation was proposed by Varadarajan et al (2015). In this work, the author tested a modi ed version of BPSO called Exponential Binary Particle Swarm Optimization (EBPSO) to select the features extracted using Block based Additive Fusion.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…In this branch, we can cite the Particle Swarm Optimization (PSO) algorithm (Eberhart and Shi;, one of the wellknown algorithms among researchers, and it is inspired by the coordinated movement of sh schools and bird ocks. Among many versions of PSO, its binary version has been widely used to nd the most discriminative set of features in facial images improving FR systems (Vora et al;Varun et al;Varadarajan et al;. Another popular SIbased algorithm is the Ant Colony Optimization (ACO) (Dorigo and Stutzle;2003), which is inspired by the collective behavior of ants in nding the shortest path between the nest and the food source through a substance called pheromone.…”

Section: Introductionmentioning

confidence: 99%

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

Plichoski

Chidambaram

Parpinelli

2018

RBCA

View full text Add to dashboard Cite

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.

show abstract

“…The combination of Chirp Z-Transform (CZT) and Goertzel algorithm are used as the preprocessing technique to normalize the image [18]. The input image is divided into specific number of same sized blocks and CZT is applied to every individual blocks.…”

Section: Chirp Z-transform (Czt)mentioning

confidence: 99%

A Survey on Different Feature Extraction and Classification Techniques Used in Image Steganalysis

babu¹,

Rangu²,

Manogna³

2017

JIS

View full text Add to dashboard Cite

Steganography is the process of hiding data into public digital medium for secret communication. The image in which the secret data is hidden is termed as stego image. The detection of hidden embedded data in the image is the foundation for blind image steganalysis. The appropriate selection of cover file type and composition contribute to the successful embedding. A large number of steganalysis techniques are available for the detection of steganography in the image. The performance of the steganalysis technique depends on the ability to extract the discriminative features for the identification of statistical changes in the image due to the embedded data. The issue encountered in the blind image steganography is the non-availability of knowledge about the applied steganography techniques in the images. This paper surveys various steganalysis methods, different filtering based preprocessing methods, feature extraction methods, and machine learning based classification methods, for the proper identification of steganography in the image.

show abstract

Face Recognition Using Block Based Feature Extraction with CZT and Goertzel-algorithm as a Preprocessing Technique

Cited by 12 publications

References 12 publications

Features for Cross Spectral Image Matching: A Survey

Features for Cross Spectral Image Matching: A Survey

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

A Survey on Different Feature Extraction and Classification Techniques Used in Image Steganalysis

Contact Info

Product

Resources

About