An Empirical Study of Dimensionality Reduction Methods for Biometric Recognition

Kerdprasop, Nittaya; Chanklan, Ratiporn; Hirunyawanakul, Anusara; Kerdprasop, Kittisak

doi:10.1109/sectech.2014.14

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…In this process, a direct estimation is made on the basis of the co-variance matrix extracted multi-sized data set with the help of non-linear optimization. Kerdprasop et al (2014) researched on the accuracy in recognition and times of executions of two distinct types of multi-dimensionality reduction techniques, viz. LDA (Linear Discriminate Analysis) and PCA (Principle Control Analysis) [9].…”

Section: A Linear Dimensionality Reductionmentioning

confidence: 99%

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Review of Dimensionality Reduction Techniques in Data Mining from Big Data

Pratap¹

2019

IJRASET

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Big Data is a large data index that increases in volume or size with very high velocity and whose variability or complexness is multidimensional. Capturing, managing, and processing or analyzing big data is really a complicated job. At present, big data analysis has reached a new stage. This new stage is known as rapid data where an enormous volume of data in gigabytes congregates with a targeted big data structure efficiently. The present structures of big data accumulate characteristically complex streams of data with respect to the big data offering to 6Vs such as velocity, variability, volume, veracity, value, and variety. After big data processing, a resultant database to be more helpful as compared to noisy, redundant, incompatible, and raw data. Another logical aspect of reducing big data is that it normally contains a large number of variables that makes it tough to discover various patterns as per the requirements. This research paper represents a comprehensive review of diverse methods that are applied for the process of big data reduction and conjointly presents a comprehensive discussion on big data dimension reduction processes, redundancy elimination, automatic learning process, data extraction, size or volume reduction, and big data compression.

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Section: A Linear Dimensionality Reductionmentioning

confidence: 99%

“…Kerdprasop et al (2014) researched on the accuracy in recognition and times of executions of two distinct types of multi-dimensionality reduction techniques, viz. LDA (Linear Discriminate Analysis) and PCA (Principle Control Analysis) [9]. They applied these two techniques to biometric image data.…”

Section: A Linear Dimensionality Reductionmentioning

confidence: 99%

Review of Dimensionality Reduction Techniques in Data Mining from Big Data

Pratap¹

2019

IJRASET

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“…PCA is a dimensionality reduction approach based on subspace projection and widely applied for image compression and recognition problems [38]. PCA has been used for extracting features from palmprint [33], face [28,11,29], and applied as reduction strategy in various biometric recognition like face, signature, fingerprint, palm print before matching [30,31,24] projected data is a collection of principal components which represents new dimensions of the data. The following steps have been applied to perform PCA based reduction:…”

Section: A Principal Component Analysismentioning

confidence: 99%

Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

Kumari*¹,

Suma²

2019

IJRTE

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Iris recognition system has gained prominent focus because of its uniqueness, stability over time. But the recognition level of single biometric based recognition systems is greatly affected by environmental conditions, physiological deficiency. Multi-biometric systems diminish this problem with the fusion of features collected from various traits or samples of the same trait, a single trait by employing multiple algorithms or multiple instances. To gain the advantages of multi-biometric systems in iris recognition, a Multi-algorithmic iris recognition system has been proposed where Texture features from iris are extracted by using 2D-Log Gabor filter and Phase features are extracted by Haar Wavelet; And these features can be integrated at various levels like Decision, Rank, Score, feature, and pixel. Even though the feature level fusion contains rich information about biometric samples when compared to remaining fusion levels; it involves mapping complexity, high dimensional feature space. To gain advantage of feature level fusion in iris recognition and to overcome the problem of resulted high dimensional feature space, Genetic Algorithm (GA) based reduction scheme, Principal Component Analysis (PCA) reduction strategy and a hybrid reduction scheme which is a combination of PCA and GA have been applied to reduce the resulted feature space. The performance of these reduction strategies have evaluated on CASIA iris database, IIT Delhi iris database using Machine Learning approaches. The results have shown that the feature space has dramatically reduced while keeping recognition accuracy and also revealed that space and time requirements have significantly decreased after employing feature reduction schemes.

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A Robust Method for Multi-algorithmic Palmprint Recognition Using Exponential Genetic Algorithm-Based Feature Selection

Palisetty¹,

Gogulamanda²

2020

Communication Software and Networks

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An Empirical Study of Dimensionality Reduction Methods for Biometric Recognition

Cited by 4 publications

References 5 publications

Review of Dimensionality Reduction Techniques in Data Mining from Big Data

Review of Dimensionality Reduction Techniques in Data Mining from Big Data

Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

A Robust Method for Multi-algorithmic Palmprint Recognition Using Exponential Genetic Algorithm-Based Feature Selection

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