Fingerprint Image Enhancement By Develop Mehtre Technique

Khalefa, Mustafa S.; Abduljabar, Zaid Amin; Zeki, Huda Ameer; Al-arab, Shatt

doi:10.5121/acij.2011.2616

Cited by 14 publications

(7 citation statements)

References 15 publications

(17 reference statements)

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“…Suppose p represents normalized histogram (NH) of f. Figure 9 shows the HE, where the region of interest and histogram equalization are illustrated in Figure 9(a) and Figure 9(b), respectively. The ( 2) and ( 3) apply for HE [27], [28]. -Median filter sub-phase: In this sub-phase, after applies of the HE for the earprint image, it will utilize a median filter that reduces the blurring of edges.…”

Section: Figure 6 Logical Indexing Examplementioning

confidence: 99%

Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features

Saleh

Mustafa²,

Chyad³

2023

IJEECS

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<span lang="EN-US">Human ear recognition can be defined as a branch of biometrics that uses images of the ears to identify people. This paper provides a new ear print recognition approach depending on the combination of gradient direction pattern (GDP2) and difference theoretic texture features (DTTF) features. The region of interest (ROI), the gray scale of the ear print was cut off, noise removal by the median filter, histogram equalization, and local normalization (LN) are the first steps in this approach. After the image has been processed, it is used as input for the fusion of GDP2 and DTTF for extracting the features of ear print images. Lastly, the Gaussian distribution (GD) was utilized to compute the distance among fusion feature vectors (FV) for ear print images for recognizing ear print images for people using a set of images that had been trained and tested. The unconstrained ear recognition challenge (UERC) database, which comprises 330 subjects for ear print images, provides the approach that was suggested by employing ear print databases. Furthermore, experimental results on images from a benchmark dataset reveal that statistical-rely super-resolution methods outperform other algorithms in ear recognition accuracy, which was around 93.70% in this case.</span>

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Section: Figure 6 Logical Indexing Examplementioning

confidence: 99%

Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features

Saleh

Mustafa²,

Chyad³

2023

IJEECS

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“…Mustafa Salah Khalefa, et al, [14] proposed fingerprint image enhancement using developed Mehter technique. Mehter technique is the combination of block filtering, histogram equalization and high pass filtering methods.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Fingerprint Image Enhancement using Gabor Filtering with Different Orientation Field Values

Pakutharivu

Srinath

2017

IJEECS

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“…Kernel High Pass Filtering adalah kernel untuk menapis nilai dengan intensitas yang tinggi serta menguranginya dengan nilai intensitas yang rendah, sehingga dapat menghasilkan efek penajaman citra (Makandar, 2015). Kernel dirancang untuk menambah kecerahan suatu piksel berdasarkan andil dari nilai-nilai tetangganya Khalefa (Khalefa, 2011).…”

Section: Pendahuluanunclassified

Efek High Pass Filtering Dengan Koefesien Nol Pada Citra Biner

Mulyana¹

2017

JMSTKIK

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Filtering akan melakukan penapisan terhadap nilai tertentu. Nilai intensitas yang dimiliki oleh citra biner hanya mempunyai nilai 1 dan 0. Filtering pada citra biner dengan menggunakan high pass filtering dengan total koefesien nol akan menghasilkan pemisahan piksel tepi dengan piksel objek yang tegas daripada filtering pada citra grayscale. Tujuan dilakukannya penelitian ini untuk mencermati efek high pass filtering dengan koefesien nol terhadap citra biner. Penelitian dilakukan dengan melakukan proses konvolusi terhadap citra yang dibinerkan terlebih dahulu. Variasi objek hitam dengan latar putih, maupun objek putih dengan latar hitam, jika dioperasikan dengan variasi kondisi core filter berupa nilai core positif maupun nilai core negatif akan sama-sama menghasilkan piksel tepi objek yang berwarna putih dan piksel yang bukan tepi objek baik pada latar maupun objek menjadi berwarna hitam. Hasil penelitian ini menghasilkan keberhasilan 100% terhadap dugaan awal mengenai efek high pass filtering dengan koefesien nol terhadap citra biner. Dugaan awal tersebut adalah variasi efek tersebut menghasilkan tepi di bagian luar objek dan tepi di bagian dalam objek. Filter dengan core positif pada citra biner dengan objek hitam dan latar putih akan menghasilkan tepian dibagian luar dari objek. Hal yang sama terjadi pada filter dengan core negatif, pada citra biner dengan objek putih dengan latar hitam. Sedangkan Filter dengan core positif pada citra biner dengan objek putih dan latar hitam akan menghasilkan tepian di bagian dalam dari objek, demikian juga filter dengan core negatif, pada citra biner dengan objek hitam dengan latar putih.

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Fingerprint Image Enhancement By Develop Mehtre Technique

Abstract: ABSTRACT

Cited by 14 publications

References 15 publications

Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features

Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features

Analysis of Fingerprint Image Enhancement using Gabor Filtering with Different Orientation Field Values

Efek High Pass Filtering Dengan Koefesien Nol Pada Citra Biner

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