Issue No. 10 - October (2011 vol. 33)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.49
T. Theoharis , Dept. of Inf. & Telecommun., Univ. of Athens, Athens, Greece
P. Perakis , Dept. of Inf. & Telecommun., Univ. of Athens, Athens, Greece
G. Passalis , Dept. of Inf. & Telecommun., Univ. of Athens, Athens, Greece
I. A. Kakadiaris , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
The uncontrolled conditions of real-world biometric applications pose a great challenge to any face recognition approach. The unconstrained acquisition of data from uncooperative subjects may result in facial scans with significant pose variations along the yaw axis. Such pose variations can cause extensive occlusions, resulting in missing data. In this paper, a novel 3D face recognition method is proposed that uses facial symmetry to handle pose variations. It employs an automatic landmark detector that estimates pose and detects occluded areas for each facial scan. Subsequently, an Annotated Face Model is registered and fitted to the scan. During fitting, facial symmetry is used to overcome the challenges of missing data. The result is a pose invariant geometry image. Unlike existing methods that require frontal scans, the proposed method performs comparisons among interpose scans using a wavelet-based biometric signature. It is suitable for real-world applications as it only requires half of the face to be visible to the sensor. The proposed method was evaluated using databases from the University of Notre Dame and the University of Houston that, to the best of our knowledge, include the most challenging pose variations publicly available. The average rank-one recognition rate of the proposed method in these databases was 83.7 percent.
wavelet transforms, face recognition, pose estimation, stereo image processing, wavelet-based biometric signature, facial symmetry, pose variation, 3D face recognition, biometric application, unconstrained data acquisition, uncooperative subjects, facial scan, yaw axis, landmark detection, pose estimation, occluded area detection, annotated face model, pose invariant geometry image, interpose scan, Shape, Face, Three dimensional displays, Nose, Face recognition, Indexes, physically-based modeling., Biometrics, face and gesture recognition
T. Theoharis, P. Perakis, G. Passalis and I. A. Kakadiaris, "Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1938-1951, 2011.