AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces

Understand

Gender classification aims at recognizing a person's gender.

• Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets.
• In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition.
• Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial features and a holistic feature which we call the foggy face.