Sistem Presensi Kelas Menggunakan Pengenalan Wajah Dengan Metode Haar Cascade Classifier

Prathivi, Rastri; Kurniawati, Yunita

doi:10.24176/simet.v11i1.3754

Cited by 19 publications

(19 citation statements)

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“…Banyak pula metode dan algoritma yang digunakan untuk proses pengenalan tesebut. Penelitian Prathivi [1] mengembangkan sistem presensi di kelas menggunakan pengenalan wajah dengan metode Haar Cascade Classifier hasilnya memiliki presentasi sebesar 75%. Penelitian Al-Aidid & Pamungkas [2] lainnya menggabungkan metode Haar Cascade Classifier sebagai pendeteksi wajah pada citra dan algoritma local binary pattern histogram sebagai pengenalan wajahnya.…”

Section: Kata Kunci Android Computer Visionunclassified

Aplikasi Pengenalan Citra Wajah di KTP Menggunakan Google Cloud Vision API dan Kairos API Berbasis Android

Guntara

2022

ilkomnika

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Berbagai penelitian sudah mencoba beberapa metode dan algoritma untuk proses pendeteksian dan pengenalan citra wajah pada manusia. Pada penelitian ini akan memanfaatkan API Google Cloud Vision dan API Kairos untuk pendeteksian dan pengenalan wajah pada foto KTP. API Google Cloud Vision merupakan arsitektur maching learning yang dikembangkan oleh Google untuk pengolahan data berbasis visual (computer vision) seperti OCR, Face detection, Image Retrieval, dan sebagainya. Sedangkan API Kairos adalah sebuah layanan API yang berfokus pada pengenalan dan verifikasi citra wajah. Hasil pengujian pada penelitian ini menunjukkan tingkat akurasi hingga 100% dalam 10 kali percobaan menggunakan citra KTP yang berbeda. Sedangkan hasil pengujian kueioner kepada 50 responden mendapatkan nilai persentasse sebesar 4.4 dari 5.

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Section: Kata Kunci Android Computer Visionunclassified

Aplikasi Pengenalan Citra Wajah di KTP Menggunakan Google Cloud Vision API dan Kairos API Berbasis Android

Guntara

2022

ilkomnika

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“…This design is employed to recognize objects based on the simple value of the feature, excluding the pixel value of the object's image, having the advantage of steadfast computation, as it solely depends on the number of pixels in a square not every pixel value of an image. [13] To design a model, detected using the haar cascade method, the 2 images are required, including positive and negative traffic sign samples. This study utilizes a positive sample of 1000 data and a negative sample of 1400 data.…”

Section: Haar Cascade Designmentioning

confidence: 99%

Autonomous car steering control and sign detection utilizing Haar Cascade and PID

Hasani¹,

Putra²,

Setyawan³

2022

AIP Conference Proceedings

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This study applies methods of Image processing, Haar Cascade, and PID in building a system on an autonomous car. The working system on an autonomous car has currently gained interest for the exploration. The Image processing method is utilized to detect road markings applying algorithms (BGR to RGB, Perspective transformation, threshold, canny edge, and histogram) in navigating pixel parameter values with actual distances. The parameter values in the line detection results are applied for input to the PID system, in controlling the handling of autonomous car of five seconds settling time and 1.5 cm overshoot. On the other hand, the Haar Cascade image training requires the positive (1000) and negative (1400) sample images to obtain a statistical model. Regarding the image processing test results, the correlation between the distance of the road markings and the camera pixels is 1 cm/10 pixels. This correlation hence demonstrates an accuracy of 0.1cm/pixel. The statistic test results for detecting traffic signs are obtained at a distance of 5 cm to 120 cm, with the optimal detection results at a distance of 5 cm to 90 cm. Regarding the traffic signs' point of view at an angle of to optimal detection results are obtained at an angle of to . Meanwhile, when testing dynamically, the detection performance depends on the angle and distance of the reading as on the results of the static test and the appropriate lighting factor on the results of training sample data on traffic signs. In addition, the results of the average accuracy of traffic sign detection are 92.3%.

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“…Haar algorithm is a method that uses statistical in face detection. The method uses sample haar-like features [18]. The haar-like feature is one of the algorithms in image processing that is generally used for face recognition by learning to recognize parts of the face such as eyes, nose, and lips [19].…”

Section: Software Designmentioning

confidence: 99%

“…A lot of research in the field of facial recognition has been done [14]. One of the methods used in facial recognition systems is Haar Cascade Classifier as the face detector [15][16][17][18]. Besides being used as a face detector, this method could be used to detect other objects.…”

Section: Introductionmentioning

confidence: 99%

Low Cost System for Face Mask Detection Based Haar Cascade Classifier Method

Labib

Hadi

Widayaka

2021

matrik

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In December 2019, there was a pandemic caused by a new type of coronavirus, namely SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) spread almost throughout the world. The World Health Organization (WHO) named it COVID-19 (Coronavirus Disease). To minimize the spread of the COVID-19, the Indonesian government announced a policy for the social distancing of 1-2 meters and wearing a medical mask. In this study, a mask detection system was built using the Haar Cascade Classifier method by detecting the facial areas such as the nose and lips. The study aims to distinguish between using masks and on the contrary. It is expected that the mask detection system can be implemented to provide direct warnings to people who do not wear masks in public areas. The results using the Haar Cascade Classifier method show that the system designed is able to detect faces, noses, and lips at a light intensity of 80-140 lux. The face is detected at a distance of 30-120cm, while the nose is at a distance of 30-60cm, while the lips are at a distance of 30-70cm. The system designed can perform the detection process at a speed of 5 fps. The overall test results obtained a success rate of 88,89%.

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Sistem Presensi Kelas Menggunakan Pengenalan Wajah Dengan Metode Haar Cascade Classifier

Cited by 19 publications

References 0 publications

Aplikasi Pengenalan Citra Wajah di KTP Menggunakan Google Cloud Vision API dan Kairos API Berbasis Android

Aplikasi Pengenalan Citra Wajah di KTP Menggunakan Google Cloud Vision API dan Kairos API Berbasis Android

Autonomous car steering control and sign detection utilizing Haar Cascade and PID

Low Cost System for Face Mask Detection Based Haar Cascade Classifier Method

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