A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face recognition

Musa, Purnawarman; Rafi, Farid Al; Lamsani, Missa

doi:10.1109/iac.2018.8780492

Cited by 50 publications

(25 citation statements)

References 5 publications

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“…5 b ) using algorithms found in [47, 48] and preprocessing result ( Fig. 5 c ) using CLAHE algorithm [44].…”

Section: Resultsmentioning

confidence: 99%

“…In the training phase, database (images) quality is critical to the recognition systems. Many enhancement techniques exist such as contrast‐limited adaptive histogram equalisation (CLAHE) [44], which is used as a preprocessing block to obtain more informative data (improved images quality). The main block of the biometric system is those dedicated to features extraction step, which allows to get the pertinent information from FV and FKP modalities.…”

Section: Proposed Architecturesmentioning

confidence: 99%

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Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

Daas

Yahi

Bakir

et al. 2020

IET Image Processing

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Recognition systems using multimodal biometrics attracts attention because they improve recognition efficiency and high-security level compared to the unimodal biometrics system. In this study, the authors present a secure multimodal biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion. The features extraction for FKP and FV are performed using transfer learning CNN architectures: AlexNet, VGG16, and ResNet50. The key step aims to select separate features descriptors from each unimodal biometrics modality. After that, the authors combine them using the proposed fusion approaches were support vector machine or Softmax applies as classifiers to increase the proposed system security. The efficiency of the proposed algorithms is tested using publicly available biometrics databases. The experimental results show that the proposed fusion architectures achieve an accuracy of 99.89% and an equal error rate of 0.05%. The obtained results indicate that the biometrics recognition system using deep learning is secure, robust, and reliable.

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“…5 b ) using algorithms found in [47, 48] and preprocessing result ( Fig. 5 c ) using CLAHE algorithm [44].…”

Section: Resultsmentioning

confidence: 99%

Section: Proposed Architecturesmentioning

confidence: 99%

Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

Daas

Yahi

Bakir

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…This approach was used in the works of [17][18][19]. Other applications of the CLAHE algorithm include the enhancement of thermal images [20], face recognition systems [21], systems to improve images captured in adverse weather conditions (fog, drizzle) [22], and improving image quality for digital side mirror cameras in vehicles [23].…”

Section: Applications Of Clahementioning

confidence: 99%

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

et al. 2022

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One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.

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“…CLAHE merupakan teknik pengolahan citra yang digunakan untuk meningkatkan kontras pada gambar. CLAHE beroperasi pada area kecil dalam gambar yang dikenal dengan tile dari pada melakukan operasi pada keseluruhan gambar [13]. Selain itu permasalahan peningkatan kontras yang berlebihan dapat diatasi dengan menggunakan CLAHE, yaitu dengan memberikan nilai batas pada histogram.…”

Section: Pendahuluanunclassified

Klasifikasi Citra X-Ray Toraks Dengan Menggunakan Contrast Limited Adaptive Histogram Equalization Dan Convolutional Neural Network (Studi Kasus: Pneumonia)

Widiarto¹,

Saputra²,

Dewi³

2021

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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3) ABSTRAK Pneumonia merupakan penyakit yang menyerang paru-paru. Ketika seseorang dicurigai sebagai penderita pneumonia maka akan dilakukan berbagai pemeriksaan untuk memastikan hasil diagnosis, salah satunya yaitu pemeriksaan pada citra x-ray toraks. Namun, terdapat kemungkinan dokter/radiologis melakukan kesalahan dalam melakukan interpretasi. Untuk meminimalisir hal tersebut diperlukan terbososan guna membantu dokter/radiologis dalam menganalisis citra x-ray toraks. Salah satunya adalah dengan menerapkan penggunaan Convolutional Neural Network (CNN), dimana harapannya CNN dapat digunakan untuk mengenali citra x-ray toraks sehat dan berpneumonia. Akan tetapi terdapat faktor yang dapat menyebabkan citra x-ray menjadi buruk, sehingga dimungkinkan dapat mempengaruhi hasil perolehan CNN. Untuk mengatasi hal tersebut Contrast Limited Adaptive Histogram Equalization (CLAHE) digunakan untuk melakukan perbaikan citra sebelum citra diterapkan pada CNN. Selain itu penggunaan beberapa epoch dan ukuran gambar yang berbeda juga diterapkan untuk mengetahui pengaruh pada hasil yang diperoleh model, dimana kemudian hasil-hasil yang diperoleh tersebut dilakukan analisis untuk mengetahui model mana yang memperoleh hasil terbaik. Setelah dilakukan pengujian, diperoleh hasil perolehan terbaik pada model dengan penerapan CLAHE pada epoch 180 dengan ukuran 256x256 yang memperoleh tingkat akurasi sebesar 95,21%.

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A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face recognition

Cited by 50 publications

References 5 publications

Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

Klasifikasi Citra X-Ray Toraks Dengan Menggunakan Contrast Limited Adaptive Histogram Equalization Dan Convolutional Neural Network (Studi Kasus: Pneumonia)

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