PROJECT TITLE :

Pose-invariant gender classification based on 3D face reconstruction and synthesis from single 2D image

ABSTRACT:

A novel method is proposed for create-invariant gender classification based mostly on three-dimensional (3D) face reconstruction from solely 2D frontal pictures. A 3D face model is reconstructed from only one 2D frontal image. Then, for each two-class of gender in the database, a feature library matrix (FLM) is formed from yaw face poses by rotating the 3D reconstructed models and extracting options in the rotated face. Every FLM is subsequently rendered based on the yaw angles of face poses. Then, an array of the FLM is chosen based on the estimated yaw angles for every category of gender. Finally, the chosen arrays from FLMs are compared with target image features by support vector machine classification. Promising results are acquired to handle cause in gender classification on the offered compared with the state-of-the-art methods.


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