Gender Identification Using Keras Model Through Detection of Face

Gender identification is a critical topic in which research is still ongoing. Many gender identification systems have been developed utilizing various designs. With the help of the Raspberry Pi 4 Model B and Raspberry Pi Camera Module V2, this paper provides a real-time system for gender identification from images. Gender identification from face images has become a significant issue in recent years. In computer vision, various practical techniques are being explored to address such a difficult challenge. The face characteristic acquired is sent into the neural network as input or test data. The neural network was created to extract features and to function as a classifier to detect genders. However, the majority of these methods fall short of great precision and accuracy. With Python as the programming language, several functions such as OpenCV, Keras, and TensorFlow were utilized to assess the effectiveness of the design. A thousand samples were tested for foreign and Filipino datasets, yielding a training accuracy of nearly 90 percent and less than 1 percent loss accuracy. As a result, the system is a reliable device for determining a user’s gender.