Multinomial Naive Bayes for real-time gender recognition
Diego Vergara, Sergio Hernández, Felipe Jorquera · 2016
Existing implementations of face recognition systems are created under controlled environments and tested using a limited amount of data. Also, these techniques have a high computational cost which forbids incremental learning that is required in real-time. We propose a gender estimation implementation based on Multinomial Naive Bayes and Local Binary Patterns. The method is tested in a modern age and gender recognition dataset with realistic examples. In order to get state-of-the-art results, Adaboost is also proposed and tested.