- Author:
- David J. Beymer
While researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing faces for the last 20 years, most systems specialize on frontal views of the face. We present a face recognizer that works under varying pose, the difficult part of which is to handle face rotations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. Our system has achieved a recognition rate of 98% on a data base of 62 people containing 10 testing and 15 modelling views per person.
Cited By
- Sayeed F and Hanmandlu M (2017). Properties of information sets and information processing with an application to face recognition, Knowledge and Information Systems, 52:2, (485-507), Online publication date: 1-Aug-2017.
- Dornaika F, Chahla C, Khattar F, Abdallah F and Snoussi H (2018). Discriminant sparse label-sensitive embedding, Engineering Applications of Artificial Intelligence, 50:C, (168-176), Online publication date: 1-Apr-2016.
- Asfaw Y, Scott G, Pelletier P and Adler A (2018). Method to evaluate pose variability in automatic face recognition performance, International Journal of Biometrics, 4:4, (373-387), Online publication date: 1-Oct-2012.
- Ebrahimpour R, Kabir E and Yousefi M (2018). Teacher-directed learning in view-independent face recognition with mixture of experts using overlapping eigenspaces, Computer Vision and Image Understanding, 111:2, (195-206), Online publication date: 1-Aug-2008.
- Yue Z, Zhao W and Chellappa R (2008). Pose-encoded spherical harmonics for face recognition and synthesis using a single image, EURASIP Journal on Advances in Signal Processing, 2008, (1-18), Online publication date: 1-Jan-2008.
- Ebrahimpour R, Kabir E and Yousefi M View-based eigenspaces with mixture of experts for view-independent face recognition Proceedings of the 7th international conference on Multiple classifier systems, (131-140)
- Ebrahimpour R, Kabir E and Yousefi M Teacher-Directed Learning with Mixture of Experts for View-Independent Face Recognition Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science, (601-611)
- Wang Y and Chua C (2005). Face recognition from 2D and 3D images using 3D Gabor filters, Image and Vision Computing, 23:11, (1018-1028), Online publication date: 1-Oct-2005.
- Chen L, Kukharev G and Ponikowski T The PCA reconstruction based approach for extending facial image databases for face recognition systems Enhanced methods in computer security, biometric and artificial intelligence systems, (215-227)
- Ullman S and Bart E (2018). Recognition invariance obtained by extended and invariant features, Neural Networks, 17:5-6, (833-848), Online publication date: 1-Jun-2004.
Zhao W, Chellappa R, Phillips P and Rosenfeld A (2003). Face recognition, ACM Computing Surveys (CSUR), 35:4, (399-458), Online publication date: 1-Dec-2003.- Heisele B, Ho P, Wu J and Poggio T (2003). Face recognition, Computer Vision and Image Understanding, 91:1-2, (6-21), Online publication date: 1-Jul-2003.
- Féraud R, Bernier O, Viallet J and Collobert M (2001). A Fast and Accurate Face Detector Based on Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:1, (42-53), Online publication date: 1-Jan-2001.
- Ratan A, Grimson W and Wells W (2000). Object Detection and Localization by Dynamic Template Warping, International Journal of Computer Vision, 36:2, (131-147), Online publication date: 1-Feb-2000.
- Vetter T (1998). Synthesis of Novel Views from a Single Face Image, International Journal of Computer Vision, 28:2, (103-116), Online publication date: 1-Jun-1998.
- Marcone G, Martinelli G and Lancetti L (2019). Eye Tracking in Image Sequences by Competitive Neural Networks, Neural Processing Letters, 7:3, (133-138), Online publication date: 1-Jun-1998.
- Rao R and Ballard D Natural basis functions and topographic memory for face recognition Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1, (10-17)
Recommendations
Face recognition under varying illumination using gradientfaces
In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the Gradientfaces. Theoretical analysis shows Gradientfaces is an illumination insensitive measure, and ...
Robust face recognition from images with varying pose
ICIAR'05: Proceedings of the Second international conference on Image Analysis and RecognitionRecognition of faces under varied poses has been a challenging area of research due to the complex dispersion of poses in feature space when compared to that of frontal faces. This paper presents a novel and robust pose-invariant face recognition method ...
2D Pose-Invariant Face Recognition Using Single Frontal-View Face Database
AbstractPersonal identification systems that use face recognition work well for test images with frontal view face, but often fail when the input face is a pose view. Most face databases come from picture ID sources such as passports or driver’s licenses. ...