IJIGSP Vol. 4, No. 8, 8 Aug. 2012
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Biometrics, Face, Face Sensor, Feature Extraction, Template Matching
Biometrics is measurable characteristics specific to an individual. Face detection has diverse applications especially as an identification solution which can meet the crying needs in security areas. While traditionally 2D images of faces have been used, 3D scans that contain both 3D data and registered color are becoming easier to acquire. Before 3D face images can be used to identify an individual, they require some form of initial alignment information, typically based on facial feature locations. We follow this by a discussion of the algorithms performance when constrained to frontal images and an analysis of its performance on a more complex dataset with significant head pose variation using 3D face data for detection provides a promising route to improved performance.
V.K. NARENDIRA KUMAR, B. SRINIVASAN, "New Biometric Approaches for Improved Person Identification Using Facial Detection", IJIGSP, vol.4, no.8, pp.43-49, 2012. DOI: 10.5815/ijigsp.2012.08.06
[1]A.K.Jian, R.Bolle, S.Pankanti(Eds), “Biometrics-personal identification in networked society” 1999, Norwell, MA: Kluwer.
[2]C.Hesher, A.Srivastava, G.Erlebacher, “A novel technique for face recognition using range images” in the Proceedings of Seventh International Symposium on Signal Processing and Its Application, 2003.
[3]K. Bowyer, K.Chang, P. Flynn, “A survey of approaches to 3D and multi-modal 3D+ 2D face recognition” in IEEE International Conference on Pattern Recognition, 2004: pages 358-361.
[4]P.Ekman, W. Friesen, “Constants across cultures in the face and emotion,” in Jounal of Personality and Social Psychology, 1971. 17(2): pages 124-129.
[5]C.Li, A.Barreto, “Profile-Based 3D Face Registration and Recognition”. in Lecture Notes on Computer Science, 2005. 3506: pages 484-494.
[6]C.Li, A.Barreto, J.Zhai, and C.Chin, “Exploring Face Recognition Using 3D Profiles and Contours” in the Proceedings of IEEE Southeast on 2005: pages 576-579.
[7]C. Garcia and M. Delakis, “Convolutional face finder: A neural architecture for fast and robust face detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1408–1423, Nov. 2004.
[8]R. Wang, J. Chen, S. Yan, S. Shan, X. Chen, and W. Gao, “Face detection based on the manifold,” in Audio- and Video-Based Biometric Person Authentication. Berlin, Germany: Springer-Verlag, 2005, pp. 208–218.
[9]R. Osadchy, M. Miller, and Y. LeCun, “Synergistic face detection and pose estimation with energy-based model,” in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2005,pp. 1017–1024.