Fuzzy Based Noise Removal, Age Group and Gender Prediction with CNN

Abstract

Facial recognition systems, which are a type of biometric systems, use the person’s facial features to identify people, and determine the information in the position between the eyes, nose, cheekbones, jawline, chin and the like and reveal a personal numerical model. The main purpose of this study is to determine the gender and age group of a selected image. By making necessary studies on the image, it is aimed to separate the gender of the person and to extract child or adult information. This study is proposed as research that performs noise reduction using the fuzzy logic fire filter algorithm and classifies the result by gender using a convolutional neural network (CNN), matrix completion and deep learning techniques. CNN algorithm, which is one of the deep facial recognition algorithms that can be used in the recognition of facial images, is the algorithm used in the study. In this study, the data obtained after the cleaning of the noises in the photographs were classified. The fuzzy logic fire filter algorithm is used to remove the noise in the image, and the image is classified according to gender and age range with the help of CNN and TensorFlow algorithms. The system has been trained using certain learning and test data sets and these algorithms. The image entered into the system is converted into a matrix expression and compared with the matrix expressions on the trained system. According to the comparison result, the image is classified as male, female, boy or girl, which matrix is closer to the value group. The data we obtained as a result of our work and the success rates show the applicability of the study. The main purpose of the study is to make gender and age group estimations according to the image with CNN classifications.