TY - GEN
T1 - Facial 3D model registration under occlusions with sensiblepoints-based reinforced hypothesis refinement
AU - Wu, Yuhang
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
Acknowledgement This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-BSH001. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project “Image and Video Person Identification in an Operational Environment: Phase I” awarded to the University of Houston. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Registering a 3D facial model to a 2D image under occlusion is difficult. First, not all of the detected facial landmarks are accurate under occlusions. Second, the number of reliable landmarks may not be enough to constrain the problem. We propose a method to synthesize additional points (Sensible Points) to create pose hypotheses. The visual clues extracted from the fiducial points, non-fiducial points, and facial contour are jointly employed to verify the hypotheses. We define a reward function to measure whether the projected dense 3D model is well-aligned with the confidence maps generated by two fully convolutional networks, and use the function to train recurrent policy networks to move the Sensible Points. The same reward function is employed in testing to select the best hypothesis from a candidate pool of hypotheses. Experimentation demonstrates that the proposed approach is very promising in solving the facial model registration problem under occlusion.
AB - Registering a 3D facial model to a 2D image under occlusion is difficult. First, not all of the detected facial landmarks are accurate under occlusions. Second, the number of reliable landmarks may not be enough to constrain the problem. We propose a method to synthesize additional points (Sensible Points) to create pose hypotheses. The visual clues extracted from the fiducial points, non-fiducial points, and facial contour are jointly employed to verify the hypotheses. We define a reward function to measure whether the projected dense 3D model is well-aligned with the confidence maps generated by two fully convolutional networks, and use the function to train recurrent policy networks to move the Sensible Points. The same reward function is employed in testing to select the best hypothesis from a candidate pool of hypotheses. Experimentation demonstrates that the proposed approach is very promising in solving the facial model registration problem under occlusion.
UR - http://www.scopus.com/inward/record.url?scp=85046275920&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046275920&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2017.8272734
DO - 10.1109/BTAS.2017.8272734
M3 - Conference contribution
AN - SCOPUS:85046275920
T3 - IEEE International Joint Conference on Biometrics, IJCB 2017
SP - 493
EP - 502
BT - IEEE International Joint Conference on Biometrics, IJCB 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Joint Conference on Biometrics, IJCB 2017
Y2 - 1 October 2017 through 4 October 2017
ER -