Abstract
Measurement of facial similarity or checking similarity is done using features. The algorithm for describing the most up-to-date and best face features for generating features is Deep Convolutional Neural Network (DCNNs). Based on this, this study uses MTCNN (Multi-task Cascaded Convolutional Neural Network) as one variation of the DCNN method. In this research, we built a research system to test results with javascript. Given the many needs that are based on mobile or can be run on a smartphone. One of them is to support the absent feature that is used in a mobile manner such as the reporting system of sales and marketing performance or members of the police personnel who normally work on a mobile basis. From the results of the tests carried out automatically using several variation models testing the image of the Aberdeen dataset as many as 60 images from 30 different people used in the face recognition research system using MTCNN with influencing image parameters such as lighting variations, object position variations, then the position taken and expression face on the object image, the research system managed to do face recognition by 100%. Thus, true positive values are equal to the amount of data tested and zero negative true values.
Highlights
Pekerjaan riset mengenal wajah serta menganalisa wajah masih merupakan pekerjaan yang cukup menantang pada komputer visi dan pengolahan citra[1]
Given the many needs that are based on mobile
support the absent feature that is used in a mobile manner
Summary
Pekerjaan riset mengenal wajah serta menganalisa wajah masih merupakan pekerjaan yang cukup menantang pada komputer visi dan pengolahan citra[1]. Proses tersebut bisa saja dilakukan dengan membuat komputer menganalisis lokasi wajah sebagai objek, ekspresi, ciri, umur serta emosi, yang terpancar di wajah, dan beberapa informasi lainnya[1]. Secara umum dalam pekerjaan analisa citra wajah terdapat tiga langkah utama yakni menentukan lokasi wajah sebagai objek, kemudian menangkap fiducial point atau titik penting wajah dan mendeskripsikan penciri atau fitur objek wajah[5]. Dalam menentukan lokasi wajah sebagai objek biasanya digunakan algoritma pendeteksi untuk mendeteksi lokasi objek wajah pada citra dan gambar bergerak dalam variasi posisi, cahaya serta ukuran[3], [6], [7]. Pada proses mendeskripsikan penciri atau fitur objek wajah biasanya metode digunakan untuk menghasilkan informasi wajah. Bahasa yang digunakan adalah javascript dengan harapan fitur ini dapat diterapkan untuk melakukan pengenalan wajah secara mobile. Salah satunya adalah untuk mendukung fitur absen yang digunakan secara mobile seperti sistem pelaporan kinerja sales dan marketing ataupun anggota personil kepolisian yang biasa bekerja secara mobile
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