Evrişimsel Sinir Ağı Modellerine Dayalı Irk ve Cinsiyet Tanıma
Viyan Mikaeel, Bülent Turan, Abdulrazaq, Maiwan · DergiPark (Istanbul University) · 2024
Researchers and developers have widely used deep learning and computer vision in many applications, including bias and representation issues, with amazing and rapid progress. In order to identify and differentiate people based on gender and ethnicity, developers employ both color concentration and facial details. This paper utilizes a new convolutional neural network model to recognize facial race. We trained and tested the model on four races and genders (African, Asian, Indian, and Caucasian). the dataset collected from the datasets (pretty-face, SCUT-FBP5500_v2., called AFD-dataset, cnsifd_faces_bmp, Indian_actors_faces, img_align_celeba, CASIA-Face-Africa). The experiment results show that Res50 models have proven to have a better model accuracy rate in races. Race Gender Convolution Neural Network (RGCNN) and IncV3 both achieved second place, while VGG19 ranked last. Both the Res50 and IncV3 results by gender show a better accuracy rate. RGCNN is in third place, while VGG19 is the last one. The RGCNN model is a lightweight has a smaller total number of parameters. The VGG19 Model, on the other hand, comes in second place. The IncV3 model, on the other hand, comes in third place, and finally, the Res50 model is the last one to have a total number of parameters.