Open Access Open Access  Restricted Access Subscription or Fee Access

Face Recognition using Transform Invariant Principal Component Analysis

R. Sindhuja, S. Arun


Face Recognition has becoming the most challenging and interesting area in all applications. It is one of the best biometric techniques. Researchers developed many best face recognition techniques. To overcome this, there should be a technique that exists during the input process to filter the valid image. This paper focuses on the automatic face alignment and ends with enhanced face recognition. This system develops a practical optimization procedure that is effective to simultaneously encode and align a large ensemble of many faces under complex variations and illuminations. This can be achieved through TIPCA algorithm; it extracts and validates the face features automatically.


Principal Component Analysis (PCA), Face Recognition, Image Ensembles, Transform Invariant PCA (TIPCA)

Full Text:



Sureshkumar.J.S:FacialRecognition.SlideShare. Accessed 12 March 2013.

Jafri,R.; Arabnia.H.R, A Survey of Face Recognition Techniques, Journal of Information Processing Systems.5, 41-68 (2009).

Application of Face Recognition,Accesssed 13 March 2007.

X. Li, T. Jia, and V. Tech, “Expression-Insensitive 3D Face Recognition using Sparse Representation,” 2575–2582, 2009.

W. Deng, J. Hu, J. Lu, and J. Guo, “Transform-Invariant PCA : A Unified Approach to Fully Automatic Face Alignment, Representation, and Recognition,” pp. 1–12.

J. Bekios-Calfa, J. Buenaposada, and L. Baumela, “Revisiting Linear Discriminant Techniques in Gender Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 4, pp. 858-864, Apr. 2011.

M.Turk A.Pentland,”Eigen Faces For Recognition”, ”Journal of Cognitive NeuroScience, vol3, no 1, pp-71-86, 1991.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.