An analysis of Haar Wavelet Transformation for androgenic hair pattern recognition

Lionnie, Regina; Alaydrus, Mudrik

doi:10.1109/iac.2016.7905683

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…We used androgenic hair database which was created by us in our previous publication [7][8]. We analyzed the system performance by using 400 images which were obtained from 25 people (all male) and 16 images from each person from the database.…”

Section: Figure 1 Flowchart Of Proposed Research Methodsmentioning

confidence: 99%

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Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition

Lionnie

Alaydrus

2016

TELKOMNIKA

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Section: Figure 1 Flowchart Of Proposed Research Methodsmentioning

confidence: 99%

“…Figure 6 shows the comparison from three publications. The first publication from [7] optimized Haar wavelet transform as the feature extraction method in 2-fold (2f-Haar) and 10-fold cross validation (10f-Haar). The research showed that the 10-fold cross validation also gave better performance (83.43 %) compared to 2-fold cross validation (71.28 %).…”

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confidence: 99%

Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition

Lionnie

Alaydrus

2016

TELKOMNIKA

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“…The Haar wavelet transform [18] is one of the simplest wavelet transforms and can be used for frequency analysis. Similar to compressed sensing, it has been employed in various fields such as image processing [29] and pattern recognition [26]. Owing to the fact that the sensitivity of the Haar wavelet transform is easy to analyze, it can be used along with the concept of differential privacy [35].…”

Section: Preliminariesmentioning

confidence: 99%

Privacy-Preserving Statistical Analysis of Genomic Data using Compressive Mechanism with Haar Wavelet Transform*

Yamamoto

Shibuya

2022

Preprint

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To promote the use of personal genome information in medicine, it is important to analyze the relationship between diseases and the human genomes. Therefore, statistical analysis using genomic data is often conducted, but there is a privacy concern with respect to releasing the statistics as they are. Existing methods to address this problem using the concept of differential privacy cannot provide accurate outputs under strong privacy guarantees, making them less practical. In this study, for the first time we investigate the application of a compressive mechanism to genomic statistical data and propose two approaches. The first is to apply the normal compressive mechanism to the statistics vector along with an algorithm to determine the number of nonzero entries in a sparse representation. The second is to alter the mechanism based on the data, aiming to release significant SNPs with a high probability. In this algorithm, we apply the compressive mechanism with the input as a sparse vector for significant data and the Laplace mechanism for non-significant data. Using the Haar transform for the wavelet matrix in the compressive mechanism is advantageous to determine the number of nonzero elements and the amount of noise. In addition, we theoretically prove that our proposed method achieves ϵ-differential privacy. We evaluated our methods in terms of accuracy, rank error, and run time compared to the Laplace and exponential mechanisms. The results show that our second method in particular can guarantee both high privacy assurance as well as utility. The Python implementation of our experiments is available at https://github.com/ay0408/CompLaplace.

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“…Pada tahun 2014, pola rambut androgenik pertama kali dikembangkan di [1] sebagai sistem untuk dijadikan ciri biometrik yang dapat melakukan identifikasi individual. Sejak tahun 2016 hingga sekarang, telah dimulai penelitian dengan menggunakan metode transformasi wavelet Haar [4], analisis komponen utama [5] skala ruang hierarki Gauss [6], metode SIFT [7] dan komparasi ruang warna [8].…”

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Studi Proses Deteksi Objek dalam Analisis Biometrik

Lionnie

Alaydrus

2019

InComTech

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Penelitian pola rambut androgenik pada penelitian ini dilakukan dengan menggunakan metode watershed segmentation berbasis gradien sobel dengan alasan pola rambut androgenik yang diteliti lebih menyerupai pola acak seperti pola pohon dan pertanian serta pola peta jaringan komunikasi dibandingkan dengan pola biometrik pada umumnya seperti pola wajah maupun pola sidik jari. Selain itu untuk mengatasi problem oversegmentation, dilakukan penapisan lolos bawah sebelum proses transformasi watershed menggunakan tapis linear jenis Gaussian dan tapis non linear jenis median. Hasil penelitian menunjukkan dengan menggunakan tapis lolos bawah, hasil deteksi terlihat lebih baik dibandingkan tidak menggunakan tapis. Tapis Gaussian memberikan hasil yang lebih baik dibandingkan tapis median dalam mendeteksi objek rambut androgenik.

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An analysis of Haar Wavelet Transformation for androgenic hair pattern recognition

Cited by 12 publications

References 13 publications

Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition

Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition

Privacy-Preserving Statistical Analysis of Genomic Data using Compressive Mechanism with Haar Wavelet Transform*

Studi Proses Deteksi Objek dalam Analisis Biometrik

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