Influence of Skin Color and Data Pre-processing on Gender Classification

Abstract

Face recognition and gender classification is one of the hot spots at present, how to get better results in the recognition process is the key. This research was based on neural networks. While some researchers have explored this issue through neural networks, this research focuses on the impact of data processing other than the model on the final results. First of all, through the manual review and screening of data sets, the training set of face images only retained deep color skin face images, and the test set did not do selection processing. The final results of the model which based on selected data set are not much different from that of the model based on the unselected training set, except for a slight decrease in the recall rate of female face recognition. Secondly, by the method of control variables, the effect of the three kinds of data pre-processing is compared. The results show that both random cropping and normalization can improve some attributes of the model to varying degrees, but only random horizontal flipping has a certain negative effect on the model. In this research, it was found that the smaller proportion of white skin face image training sets hardly affected the model performance. Data pre-processing can effectively improve the model, but the use of random horizontal flips in a data set with a great number of small tilt angle images may lead to the deterioration of the results on the training set.