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Objectives 研究目標
Performance of face recognition system will be severely degraded if input face is not frontal. To overcome this, we transformed a face with arbitrary pose to a frontal form that is suitable for face recognition.

Overall Diagram 總體框圖

Face detection overall diagram

Methodology 主要算法


Facial Feature Detection

Uses AAM with minimum residual error to locate detailed facial features


3D Modeling & Rendering

Employed 3D face mesh has 6292 vertices and 6152 facet, whose appearance could be modified by adjusting parameters


Pose estimation

Uses Moore-Penrose pseudo inverse to calculate the transformation matrix
利用Moore-Penrose 僞逆計算變換矩陣


Texture Map Rebuilding

Based on (3), texture in input image was filled and interpolated on texture map by texture mapping. A post processing is then performed for more realistic 3D face reconstruction results

Reconstruction Results 重建結果
Face images are tested with different poses in the GTAV database, non-frontal view face can be synthesized to a realistic frontal view

Reconstruction Results

Contributions 研究成果
Digital video surveillance technologies are now becoming more and more mature and thus the entry barrier for this industry is becoming lower. We believe the technologies developed in this project, which integrates well with commodity-off-the-shelf face recognition package, can realize many value-added features for Hong Kong manufacturers to differentiate themselves from the other competitors around the globe.

Team Members 研究成員
Department of Computer Science: Dr CHOW Kam Pui, Prof CHIN Yuk Lun Francis, Dr WONG Kwan Yee Kenneth
計算機科學系: 鄒錦沛博士、 錢玉麟教授、黃君義博士

Project Sponsor 贊助機構
Innovation and Technology Fund, Innovation and Technology Commission 創新科技署創新及科技基金
CyberView Inc. Ltd.

This project is supported by Cognitec Systems Ltd. which uses its FaceVAC software to evaluate our proposed menthod
此項目由Cognitec Systems Ltd. 支持並使用其FaceVACS人臉識別軟件作測試

Video 短片