Consecutive gender and age classification from facial images based on ranked local binary patterns

A new algorithm for consecutive classification of gender and age based on a two-stage support vector regression is proposed. Only most significant local binary patterns are used to describe the image. To enhance the gender classification accuracy we use bootstrapping with the training based on difficult examples, whereas the age classification is improved through the use of floating age ranges.