3 research outputs found
Deteksi Suhu Melalui Citra Termal Wajah Menggunakan Deep Learning
Dalam masa pandemi kasus penularan virus CORONA masih tetap bertambah dari hari ke hari. Salah satu gejala yang umum dialami oleh pasien COVID-19 adalah demam. Hal yang umum dilakukan untuk mengukur suhu di masa pandemi adalah menggunakan termometer non kontak. Deep Learning bisa digunakan untuk mendeteksi wajah dan membantu mendeteksi suhu maksimal wajah dari gambar termal. Tujuan Penelitian ini adalah membuat aplikasi pendeteksi suhu pada citra termal menggunakan pendekatan Deep Learning. Dalam Penelitian ini dilatih sebuah model deteksi SSD-MobileNet untuk mendeteksi area wajah dari citra termal. Setelah terdeteksi, data suhu diekstrak dari area wajah tersebut. Dalam pelaksanaan penelitian ini digunakan dataset citra termal Tuft Face Database, IRDatabase, dan citra termal yang diambil menggunakan Flir One. Dari hasil uji coba didapatkan hasil mean average precision deteksi wajah sebesar 0,95 dengan threshold dari evaluasi model untuk IoU 0,75 sebesar 0,95 dan mean absolute error deteksi suhu sebesar 1,51
Inner Eye Canthus Localization for Human Body Temperature Screening
In this paper, we propose an automatic approach for localizing the inner eye
canthus in thermal face images. We first coarsely detect 5 facial keypoints
corresponding to the center of the eyes, the nosetip and the ears. Then we
compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model
(3DMM). This correspondence is used to project the entire 3D face onto the
image, and subsequently locate the inner eye canthus. Detecting this location
allows to obtain the most precise body temperature measurement for a person
using a thermal camera. We evaluated the approach on a thermal face dataset
provided with manually annotated landmarks. However, such manual annotations
are normally conceived to identify facial parts such as eyes, nose and mouth,
and are not specifically tailored for localizing the eye canthus region. As
additional contribution, we enrich the original dataset by using the annotated
landmarks to deform and project the 3DMM onto the images. Then, by manually
selecting a small region corresponding to the eye canthus, we enrich the
dataset with additional annotations. By using the manual landmarks, we ensure
the correctness of the 3DMM projection, which can be used as ground-truth for
future evaluations. Moreover, we supply the dataset with the 3D head poses and
per-point visibility masks for detecting self-occlusions. The data will be
publicly released