Iris Recognition Based on DWT and PCA

Kumar, Deepak; Raja, K B; Nuthan, N.; Sindhuja, B.; Supriya, P.; Chhotaray, R.K.; Pattnaik, Sabyasachi

doi:10.1109/cicn.2011.102

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Variables that are not correlated are called "principal components". A basic principle of the data is revealed by these components, which constitute the biggest variance possible ( Kumar et al, 2011).…”

Section: F Principal Component Analysis (Pca)mentioning

confidence: 99%

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Hasan

Mstafa

2022

SJUOZ

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Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric. Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait, because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.

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Section: F Principal Component Analysis (Pca)mentioning

confidence: 99%

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Hasan

Mstafa

2022

SJUOZ

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“…To overcome these limitations; it is crucial to apply a compression technique to a fingerprint image that will maintain the essential information needed to reconstruct the image and reduce the cost implications. Kambli, Mansi & Bhatia [6] state that image compression enhances the image by applying some algorithms or operations to these pixel values. Since a digital image is a representation of an image in terms of pixel values or intensities values [4]; the compression process allows reducing large data files into smaller files for efficiency of storage and transmission [5].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Image Compression using Biorthogonal and Orthogonal Wavelets at Different Levels of Discrete Wavelet Transform

2024

JIEA

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Storage space has become a significant issue in this digital generation. Forensic applications such as criminal investigations, terrorist identification, and national security issues require large amounts of space to store large volumes of fingerprints and face recognition scans collected and stored every day. Ultimately, less memory space results to more time used to process and transmit an image. In this paper, fingerprint images are compressed using the DWT (discrete wavelet transform) biorthogonal wavelet Bior4.4 and orthogonal wavelet Haar at different levels of transformation. The experiment is recognized through python and is divided into two parts; transformation testing and SPIHT (Set Partitioning in Hierarchical Trees) algorithm compression. The peak signal to noise ratio (PSNR), mean square error (MSE) as well as the compression ratio (CR) are used to objectively enumerate the quality of the compressed images. It is observed that MSE and PSNR are favorable when transformation is done using Bior 4.4 first then Haar during the remaining levels as proposed rather than using one distinct wavelet for transformation.

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“…The stages of Iris recognition [21] consisted of data collection, initial data processing, feature extraction and iris matching. This research used False Rejected Rate (FRR) and Accuracy to investigate the iris recognition performance.…”

Section: Research Methodology Figure 2 Iris Recognition Process Flowmentioning

confidence: 99%

Iris recognition based on distance similarity and PCA

Sari

Alkaff

Pramunendar

2018

AIP Conference Proceedings

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Abstract.Iris is regarded as the most unique biometric identification. This paper proposes a new feature extraction based on iris texture patterns with Principal Component Analysis (PCA). PCA is used to store computing process in classification process. The focus of this paper was to compare the accuracy result of classification methods of distance measurement such as Euclidean Distance, City Block Distance, Chebyshev Distance, Canberra Distance and Bray-Curtis Distance. Accuracy test shows that PCA can be used in various classification methods that use distance measurement.

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Iris Recognition Based on DWT and PCA

Cited by 11 publications

References 7 publications

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Fingerprint Image Compression using Biorthogonal and Orthogonal Wavelets at Different Levels of Discrete Wavelet Transform

Iris recognition based on distance similarity and PCA

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