Real-time 3D face localization and verification

We present a method for real-time 3D face localization and verification using a consumer-grade depth camera. Our approach consists of three parts: face detection, head pose estimation, and face verification. Face detection is performed using a standard detection framework which we significantly improve by leveraging depth information. To estimate the pose of the detected face, we developed a technique that uses a combination of the particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm to accurately align a 3D face model to the measured depth data. With the face localized within the image, we can compare a database 3D face model to the depth image to verify the identity of the subject. We learn a similarity metric using a random decision forest to accurately authenticate the subject. We demonstrate state-of-the-art results for both face localization and face verification on standard datasets. Since the camera and our method operate at video rate, our system is capable of continuously authenticating a subject while he/she uses his/her device.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Meyer, Gregory Paul

Contributors dc:contributor

• Do, Minh N.
• Patel, Sanjay J.
• Hart, John C.
• Hoiem, Derek

• Computer Vision
• 3D
• Face Detection
• Face Verification
• Face Recognition
• Head Pose Estimation
• Depth Cameras

Statement dc:rights

• Copyright 2016 Gregory Meyer

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/95341

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/95341