Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 33; no. 10; pp. 1952 - 1961
• Main Authors: Prabhu, U., Jingu Heo, Savvides, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D face modeling; Biometric Identification - methods; Computational modeling; Databases, Factual; Face; Face - anatomy & histology; Face recognition; generic elastic models; Humans; Imaging, Three-Dimensional - methods; Linear Models; Models, Theoretical; Pose-invariant face recognition; Posture; Principal component analysis; Shape; Solid modeling; Studies; Three dimensional displays
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2011.123

Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.