Gender Classification Using a Convolutional Neural Network (CNN)

Abstract

Abstract The purpose of this paper is to demonstrate an innovative convolutional neural network (also known as CNN) methodology for real-time categorization of gender via face photos. The suggested CNN architecture boasts much reduced computational complexity than the current methodologies used in pattern recognition applications. By combining convolutional and subsampling layers, the overall processing layer count is minimised to four. Notably, using cross-correlation versus standard convolution tends to alleviate computing strain. The association is programmed using extended worldwide acquisition frequencies and a second-order backpropagation learning algorithmic framework. The demonstrated CNN approach has been examined using two freely downloadable facial statistics, SUMS and AT&T, with classification accuracies of 99.38% and 98.75%, respectively. Furthermore, the neural network's algorithm demonstrates exceptional efficiency by analysing and categorising a 32 by 32-pixel face picture in just 0.27 milliseconds, resulting in an outstanding consumption of over 3700 images per second. The successful performance of the proposed CNN methodology is further demonstrated by its speedy convergence throughout the training process, which requires less than 20 epochs. The results were produced to showcase the suggested CNN's outstanding accuracy in accurately categorising data, launching it as a realistic and effective real-time identification of the gender system.