Gender classification by LPQ features from intensity and Monogenic images

Facial biometrics systems struggle to find human biometrics cues from face images. These cues include identity, expression, age, pose and gender, to name a few. Compared with others, gender is among the most useful information since it can be used in human-machine interaction applications, marketing researches and advertising [1]. Unlike other attributes, gender has only two values, male and female, but gender recognition is one of the most challenging tasks [2].