Analisis Pendeteksian Pola Wajah Menggunakan Metode Haar-Like Feature

Chau, Sugandi; Banjarnahor, Jepri; Irfansyah, Dikky; Kumala, Sinta

doi:10.31289/jite.v2i2.2133

Cited by 12 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This method can give indication of a specific image. This method used to recognize object based on value of a feature, not by pixel value in input image (Chau, Banjarnahor, Irfansyah, Kumala, & Banjarnahor, 2019).…”

Section: Image Understandingmentioning

confidence: 99%

Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar

Robin

Handinata

Chandra³

2021

CNAHPC

View full text Add to dashboard Cite

Facial recognition is one of the most popular way to authenticate user into a system. This method is preferable considering the tendency of users for using the same password across multiple sites which made the user has already made his own account securities in vulnerable states. Using biometrics might supply solutions to solve this problem and facial recognition is one of the best biometric methods can be apply as a digital account security solution. This study to design a prototype system implementing facial recognition to verify users to measure how accurate these methods are. The method used here is Viola-Jones for face detection, Eigenface and Haar feature for face recognition from the OpenCV. The system was designed in Java. Based on the test results from the system designed, system can recognize user face with 100% accuracy if faces are shot in a well desirable condition. The system is able to recognize the user's face with various expressions including with or without glasses. However, the system has difficulty in recognizing user’s face in facing up, down, sideways position or blocked by accessories or body parts such as hands. After some experiment, it was proven that the system designed is accurate, reliable and safe enough to be implemented to digital authorization process.

show abstract

Section: Image Understandingmentioning

confidence: 99%

Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar

Robin

Handinata

Chandra³

2021

CNAHPC

View full text Add to dashboard Cite

show abstract

“…The size used in this research is 20x20, so that the convolution process will move from the top left to the right with a box size of 20x20 pixels. This picture below is the haar-like feature used in this process [8]: After the haar-like feature process, the next step is the integral image process to the images. Integration means adding small units together.…”

Section: Figure 1 Research Flowmentioning

confidence: 99%

“…Integration means adding small units together. In a small unit image that is added is the pixel value, the integral value for each is the sum of all pixels from the top left corner to the bottom right [8]. The picture below is a flowchart of the integral image process: Based on the image above, the left image is an integrated value on the pixel axis (x, y) containing the sum of all pixels in the rectangular area starting from the top left to the location of the axis (x, y).…”

Section: Figure 1 Research Flowmentioning

confidence: 99%

See 1 more Smart Citation

Detection of fake shallots using website-based haar-like features algorithm

Setyawan

Akbar

2021

Compiler

View full text Add to dashboard Cite

Shallots is commonly used as essential cooking spices or complement seasoning. The high market demand for this commodity has triggered some people to counterfeit it. They mix the shallots with defective products of onions to get more benefits. It urges to provide a system that can help people to distinguish whether the shallot is original or fake. This research aims to provides an object recognition system for fake shallots utilizing the Haar-Like Feature algorithm. It used the cascade training data set of 59 positive images and 150 negative images with 50 comparison images. The identification process of the shallots was through the haar-cascade process, integrated image, adaptive boosting, cascade classifier, and local binary pattern histogram. This system was made based on the Django website using the python programming language. The test was conducted 30 times on Brebes shallots mixed with Mumbai's mini onions in a single and mixture test method. The test obtained an average percentage of 69.2% for the object recognition of Mumbai's mini onions.

show abstract

“…Hal itu tentu kurang efektif karena terbukanya kesempatan untuk melakukan kecurangan diantara mahasiswa itu sendiri serta tidak adanya batasan keterlambatan yang pasti dari pihak perguruan tinggi dan setiap dosen-dosen yang mengajar. Selain itu, penyususnan oleh pihak Administrasi akademik juga dilakukan dengan cara konvensional yang membutuhkan waktu lama hanya karena memasukkan banyak data [3].…”

Section: Latar Belakangunclassified

Desain dan Implementasi Sistem Absensi Mahasiswa Berdasarkan Fitur Pengenalan Wajah dengan Menggunakan Metode Haar-Like Feature

2019

Self Cite

View full text Add to dashboard Cite

Absensi kehadiran mahasiswa adalah suatu kegiatan yang dilakukan mahasiswa untuk membuktikan dirinya hadir dalam kegiatan pembelajaran. Salah satu cara untuk melakukan absensi kehadiran adalah dengan cara manual. Mahasiswa melakukan tanda tangan pada lembar kehadiran yang telah dipersiapkan. Hal ini dapat menimbulkan masalah, yaitu memungkinkan terjadinya kecurangan pada saat pengisian absensi. Solusi dari permasalahan ini adalah dengan melakukan proses absensi berbasis sistem. Pada penelitian ini akan diterapkan suatu sistem pengenalan wajah berbasis desktop dan website dengan menggunakan Raspberry Pi sebagai media akses kontrol terhadap absensi bagi mahasiswa untuk mengikuti perkuliahan. Sistem pengenalan wajah yang dirancang menggunakan metode Haar-Like Feature yang memberikan indikasi secara spesifik pada sebuah gambar atau image yang digunakan untuk mengenali objek berdasarkan nilai sederhana dari fitur. Berdasarkan hasil pengujian dari aplikasi ini menunjukkan bahwa sistem absensi berbasis pengenalan wajah telah berhasil membatasi pihak yang tidak memiliki hak akses dan tidak terdata dalam database absensi. Selain itu pencatatan data kehadiran mahasiswa dapat terkomputerisasi sehingga pendataan yang dilakukan dalam waktu seminggu menjadi lebih cepat dimana sebelum sistem ini diimplementasikan proses absensi membutuhkan waktu 6-8 hari, menjadi 3-4 hari. Sistem yang dihasilkan juga memberikan proteksi terhadap penyalahgunaan.

show abstract

Analisis Pendeteksian Pola Wajah Menggunakan Metode Haar-Like Feature

Cited by 12 publications

References 0 publications

Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar

Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar

Detection of fake shallots using website-based haar-like features algorithm

Desain dan Implementasi Sistem Absensi Mahasiswa Berdasarkan Fitur Pengenalan Wajah dengan Menggunakan Metode Haar-Like Feature

Contact Info

Product

Resources

About