DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Introduction:
Most of works about face recognition is made up of 4 stages, detect, align, represent, classify. In the paper, we focus on the stage of detection and alignment, based on this method, we get the performance which is better than the state-of-the-art method and close to human-level performance.

Face Alignment:
The pipeline is briefly introduced as follows:

(a) Use 6 base points to bound face.
(b) Use another 67 points to get 3D shape face.

Feature Representation:
The frontalized crop will be the input of the following DNN architecture.

Experiment:

Dataset:

Social Face Classification (SFC), 4.4M images, 4030 people.
Labeled Face in the Wild (LFW), 13.2K images, 5749 people.
Youtube Face (YTF), 3425 Youtube videos, 1595 subjects.

Result:
DeepFace can beat state-of-the-art method and be close to human-level performance.