Comparative analysis of gender classification methods using convolutional neural networks

Gender classification has become an important application in the fields of system automation and artificial intelligence, having important implications across various fields. The main challenge in this classification task is variation in illumination that affects the quality of facial images. This study presents method for identifying genders with convolutional neural networks (CNNs). To address this issue, various preprocessing methods are applied, including self quotient image (SQI), locally tuned inverse sine nonlinear (LTISN), histogram equalization (HE), difference of gaussian (DoG), and gamma intensity correction (GIC), to stabilize the effects of illumination variations before the images are processed by CNN. The CNN architecture used consists of 5 convolutional blocks and 2 fully connected blocks, which have proven effective in image recognition. The results of study show that model trained with DoG method achieved accuracy of 91.07, making it the best preprocessing technique compared to other methods such as SQI and HE, which achieved accuracy of 90.39 and 88.76, respectively. These findings demonstrate that application of SQI in CNN can improve accuracy of gender classification on facial images, providing better performance than previous methods. These findings are expected to serve as foundation for further developments in facial image classification and its applications in various fields.