NCCP: A Logical Development of Deep Learning Based Human Gender Classification System Using Neural Convolutional Classification Principles

The most important component of a person's distinctive identification is their face. There are a lot of uses for facial recognition technology beyond just categorizing people; for example, it may be used for security concerns at banks, other organizations, and places with big public gatherings. With the meteoric rise of social media and platform usage, gender and age detection quickly sprang to the forefront. Attribute data, like gender and age, enhances face recognition accuracy. Background complexity, object occlusion, and lighting fluctuations make gender classification from facial images a difficult endeavor. Expression analysis, identification, and tracking are just a few of the many uses for face images. This paper presents a novel learning algorithm, the Neural Convolutional Classification Principle (NCCP), for determining a person's gender from their facial features. To test how well the NCCP works, it is cross-validated with the more traditional Convolutional Neural Network (CNN) model. The efficacy of the two models in classifying faces as male or female was tested experimentally. Both approaches were found to be successful for gender categorization, according to the results. In addition, a comparison was made between these two models and a few well-known gender categorization techniques.