A Review: Face Recognition Using Local Tetra Pattern
Keywords:
Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Local Derivative Pattern (LDP), Local Tetra Pattern (LTrP)Abstract
Face recognition is a critical technology in security, authentication, and surveillance systems. Traditional methods like Local Binary Pattern (LBP) and Principal Component Analysis (PCA) have limitations under variations in lighting, facial expression, and pose. This project proposes a robust face recognition system using Local Tetra Pattern (LTrP), a texture-based descriptor that encodes directional information from image gradients. The proposed method extracts LTrP features from facial images and employs machine learning for classification, ensuring high recognition accuracy under varying conditions.
Downloads
References
[1]. T. Ahonen, A. Hadid and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037-2041, 2006.
[2]. Timo Ahonen, Abdenour Hadid and Matti Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037-2041, 2006.
[3]. T. Ojala, M. Pietikainen and D. Harwood, “A Comparative Study of Texture Measures with Classification based on Feature Distributions”, Pattern Recognition, Vol. 29, No. 1, pp. 51-59, 1996.
[4]. Wen-Hung Liao and Ting-Jung Young, “Texture Classification using Uniform Extended Local Ternary Patterns”, Proceedings of IEEE International Symposium on Multimedia, pp. 191-195, 2010.
[5]. K. Thangadurai, S. Bhuvana and R. Radhakrishnan, “An Improved Local Tetra Pattern for Content based Image Retrieval”, Journal of Global Research in Computer Science, Vol. 4, No. 4, pp. 37-42,2013.
[6]. M. Turk and A. Pentland, “Eigen Faces for Recognition”, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991.
[7]. P. Belhumeur, J. Hespanha and D. Kriegman, “Eigenfaces vs. Fisherfaces: Rcoginition using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997.
[8]. The Database of Faces, Available at, http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces
[9]. Yong Rui, Thomas S. Huang and Shih-Fu Chang, “Image Retrieval: Current Techniques, Promising Directions and Open Issues”, Journal of Visual Communication and Image Representation, Vol. 10, No. 1, pp. 39-62, 1999.
[10]. Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature sets for Face Recognition under Difficult Lighting Conditions”, Proceedings of 3rd International Workshop, Analysis and Modeling of Faces and Gestures, pp. 1635-1650, 2010.
[11]. Baochang Zhang, Yongsheng Gao, Sanqiang Zhao and Jianzhuang Liu, “Local Derivative Pattern Verses Local Binary Pattern: Face Recognition with High-order Local Pattern Descriptor”, IEEE Transactions on Image Processing, Vol. 19, No.2, pp. 533-544, 2010.
[12]. W. Zhao, R. Chellappa P.J. Phillips and A. Rosenfeld, “Face Recognition: A Literature Survey”, ACM Computing Surveys, Vol. 35, No. 4, pp. 399-459, 2003.
[13]. R.Chellapa, C.L.Wilson and D.JKriegman, “Eigenfaces vs. Fisherfaces A Survey”, Proceedings of IEEE, Vol. 83, No. 5, pp. 705-740, 1995.
[14]. Hongming Zhang, Wen Gao, Xilin Chen and Debin Zhao, “Learning Informative Features for Spatial Histogram based Object Detection”, Proceedings of IEEE International Joint Conference on Neural Networks, Vol. 3, pp. 1806-1811, 2005.
[15]. S. Murala, R.P. Maheshwari and R. Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-based Image Retrieval System”, IEEE Transactions on Image Processing, Vol. 21, No. 5, pp. 2874-2886, 2012.
