Gender Recognition from Face Images with Deep Learning - Akbulut, Yaman; Sengur, Abdulkadir; Ekici, Sami (Ieee, 2017)
Gender is one of the main factors in the interaction between individuals. Recently, with the development of social media environments and smartphones, gender recognition applications have both begun to grow and become important. In many fields such as face recognition, facial expression analysis, tracking and surveillance, human-computer interaction, biometry, gender recognition applications can be seen. In this study, gender recognition was carried out from face images with deep learning. The Local Receptive Field-Extreme Learning Machine (LRF-ELM) and Convolutional Neural Networks (CNN) were used as the deep learning methods. Experiments were performed on a face data set generated for age and gender recognition. LRF-ELM and CNN achieved performance rate of 80% and 87.13%, respectively.
2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
Keywords: Gender recognition; gender classification; deep learning; CNN; LRF-ELM