Pengelompokan usia berdasarkan citra wajah menggunakan algoritma neural network dengan fitur face anthropometry dan kedalam kerutan

Hayatin, Nur

doi:10.26594/teknologi.v6i2.577

Cited by 4 publications

(7 citation statements)

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“…Wajah memiliki struktur yang sangat kompleks dan memiliki karakteristik masing-masing. Dari karakteristik tersebut, wajah manusia menyimpan informasi penting yang dapat digunakan untuk memprediksi gender, ras, usia, dll [3]. Masing-masing tahap usia perkembangan manusia dikelompokkan menjadi empat, yaitu anak-anak, ramaja, dewasa dan lansia [3].…”

Section: Pendahuluanunclassified

“…Dari karakteristik tersebut, wajah manusia menyimpan informasi penting yang dapat digunakan untuk memprediksi gender, ras, usia, dll [3]. Masing-masing tahap usia perkembangan manusia dikelompokkan menjadi empat, yaitu anak-anak, ramaja, dewasa dan lansia [3]. Seiring bertambahnya usia, wajah manusia juga mengalami perubahan mendasar seperti semakin banyak kerutan, perubahan tulang pipi dan jarak antar ciri utama wajah seperti mata, hidung dan mulut [4].…”

Section: Pendahuluanunclassified

“…"Klasifikasi Kelompok Umur Manusia Berdasarkan Analisis Dimensi Fraktal Box Counting Dari Citra Wajah Dengan Deteksi Tepi Canny". Nilai akurasi paling optimal yaitu 98,33% [3].…”

Section: Pendahuluanunclassified

“…Usia manusia dapat dibagi menjadi beberapa rentang atau kelompok seperti tabel 1 dimana masing-masing kelompok menggambarkan tahap pertumbuhan manusia tersebut. Salah satu pembagian kelompok umur atau kategori umur dikeluarkan oleh Departemen Kesehatan RI (2009) dalam situs resminya yaitu depkes.go.id sebagaimana pada tabel 1 [3]: Tabel 1. Kategori Usia [3].…”

Section: A Usiaunclassified

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Klasifikasi Usia Berdasarkan Citra Wajah Menggunakan Algoritma Artificial Neural Network dan Gabor Filter

Melangi

2020

JJEEE

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Pengklasifikasian kelompok usia dibangun berdasarkan ciri-ciri dari fitur wajah. klasifikasi usia berdasarkan citra wajah perlu dilakukan dengan lebih akurat agar dapat berguna dalam sistem pengenalan usia manusia. Beberapa kesulitan dalam pengenalan wajah yang sering muncul karena variabilitas wajah seperti ekspresi, penuaan, variasi kumis dan sebagainya. Metode filter gabor dikenal sebagai detektor ciri yang sukses serta memiliki kemampuan mengeliminasi parameter variabilitas wajah yang pada metode lainnya sering menggangggu dalam proses pengenalan. Dengan menggunakan metode Gabor filter yang terbukti handal digunakan untuk memecahkan masalah agar pengenalan usia berdasarkan wajah dapat dilakukan dengan lebih akurat. Hasil penelitian menunjukkan bahwa penerapan metode Gabor Filter dan Artificial Neural Network pada masalah pengenalan usia berdasarkan citra wajah berhasil mendapatkan akurasi yaitu sebesar 83% dengan menggunakan pengujian Confusion Matrix. Dengan demikian penerapan metode Gabor Filter dan Artificial Neural Network pada masalah pengenalan usia berdasarkan citra wajah cukup akurat, dan dapat diimplementasikan. Kata kunci: Klasifikasi Usia, Wajah, ANN, Gabor Filter. Classification of age groups is built on the characteristics of facial features. Age classifications based on facial images need to be done more accurately in order to be useful in the human age recognition system. Some difficulties in facial recognition that often arise due to facial variability such as expression, aging, mustache variations and so on. Gabor filter method is known as a successful feature detector and has the ability to eliminate facial variability parameters which in other methods often interfere in the recognition process. By using the Gabor filter method which is proven to be reliable it is used to solve problems so that face recognition based on faces can be done more accurately. The results showed that the application of the Gabor Filter and Artificial Neural Network method on the problem of age recognition based on face images managed to get an accuracy of 83% using the Confusion Matrix test. Thus the application of the Gabor Filter and Artificial Neural Network method to the problem of age recognition based on face images is quite accurate, and can be implemented.Keywords: Age Classification, Face, ANN, Gabor Filter

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Section: Pendahuluanunclassified

“…"Klasifikasi Kelompok Umur Manusia Berdasarkan Analisis Dimensi Fraktal Box Counting Dari Citra Wajah Dengan Deteksi Tepi Canny". Nilai akurasi paling optimal yaitu 98,33% [3].…”

Section: Pendahuluanunclassified

Section: A Usiaunclassified

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Klasifikasi Usia Berdasarkan Citra Wajah Menggunakan Algoritma Artificial Neural Network dan Gabor Filter

Melangi

2020

JJEEE

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“…Other studies predict age based on face images from the KinFaceW dataset [10]. The predicted age groups are children, adolescence, and oldster.…”

Section: Introductionmentioning

confidence: 99%

Digitalization of Human Head Anthropometry Measurement Using Pixels Measurement Method

Pratama

Muslim

Zul

2019

IJITEE

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Head Anthropometry is a part of anthropometry that needed to be measured carefully. It is because human head becomes an important part that necessary to be protected. The protection aims to look after the safety of the human head. Safety factors can be achieved by designing head products. Therefore, head anthropometry data is required to make a product design Currently, data retrieval of head anthropometry is still using several measuring devices such as anthropometers, sliding callipers, spreading callipers, and tape gauges. This measurement method makes the standard deviation become higher and also take a lot of time to capture huge amounts of anthropometry data. However, the problem has been resolved by other study research with building a head dimension measurement system using digital camera. But the system still need the integration with digital camera. This study uses the IP Camera that has been integrated with the system to capture human head from the front and side. The captured image is segmented into several areas based on head dimension. Then, the image is processed using pixel measurement method by performing feature extraction on each head dimension to get the result of head dimension measurement. The result shows that calliper measurement and system measurement against ten of fourteen human head anthropometry dimensions is identical with the best distance between IP Camera and the head as far as 200 cm. This head anthropometry data is expected to make a contribution to Indonesian Ergonomics Society.

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Automatic wrinkles detection on face image

Muhimmah

Abriyani

Kurniawardhani

2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Pengelompokan usia berdasarkan citra wajah menggunakan algoritma neural network dengan fitur face anthropometry dan kedalam kerutan

Cited by 4 publications

References 1 publication

Klasifikasi Usia Berdasarkan Citra Wajah Menggunakan Algoritma Artificial Neural Network dan Gabor Filter

Klasifikasi Usia Berdasarkan Citra Wajah Menggunakan Algoritma Artificial Neural Network dan Gabor Filter

Digitalization of Human Head Anthropometry Measurement Using Pixels Measurement Method

Automatic wrinkles detection on face image

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