Combination of Deep Neural Network and YuNet for Python-Based Human Lifespan Prediction - Apridiansyah, Yovi; Ardiansyah, Adidi Muhammad; Wijaya, Ardi (Jurusan Informatika, 2025)
Purpose: In this research on face detection, many methods face challenges in the accuracy of age prediction due to the complexity of facial features that are influenced by factors such as lighting, expression, and image quality. Therefore, this research focuses on developing more accurate and efficient methods by utilizing Deep Neural Network (DNN) and YuNet. The purpose of this study is to develop a face recognition model in detecting and determining human age automatically using Python with the DNN method to study facial patterns in determining human age precisely and integrate the YuNet library as a lightweight face detection framework that is efficient in the identification process.Design/methodology/approach: In this study, a system was created for predicting human age using the Deep Neural Network method which functions to predict age based on facial patterns in images and the Yunet method as a facial image detector. The stages of this research start from taking pictures, installing python libraries, namely opencv, face detection process, and age detection process.Findings/result: The results of the study show that the DNN and YuNet methods have tested as many as 50 samples in the form of photos of human faces taken at a distance of half a meter, so by using the DNN and YuNet methods, researchers have succeeded in obtaining the age of the human face through the image processing process which can then obtain an accuracy level or Precission of 80% and the accuracy level of success between the prediction value and the actual value given by the system is 80%.Originality/value/state of the art: In this study, the system uses Python tools where in the face detection process using the YuNet method, this method is used because YuNet can directly detect facial features in the image and is lightweight in operation. In terms of DNN prediction, it functions as a method that can predict age based on the results of facial image detection. In this study, a dataset was also used for 50 facial samples that were tested for accuracy using the confussion matrix by looking for precission, recal, and accuracy values.