PCA+LDA Method for Face Recognition using Neural Network
Dhiren Pandit1, Jayesh Dhodiya2
1Prof. Dhiren Pandit, Science & Humanities Dept., LDRP-ITR, Gandhinagar, India.
2Dr. Jayesh Dhodiya, AHMD, SVNIT, Surat, India.
Manuscript received on April 28, 2015. | Revised Manuscript received on May 02, 2015. | Manuscript published on May 15, 2015. | PP: 6-11 | Volume-3 Issue-6, May 2015. | Retrieval Number: F0852053615/2015©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Face recognition plays important role in many applications like video surveillance, retrieval of an identity from a database for criminal investigations and forensic applications. The face is considered as good bio-metric for many reasons: the acquisition process is non-intrusive and does not require collaboration of the subject to be recognized. The acquisition process of a face from a scene is simpler and cheaper than the acquisition of other bio-metrics as the iris and the fingerprint. On the other hand, many problems arise, because of the variability of many parameters like face expression, pose, scale, lighting, and other environmental parameters. Face recognition involved in application like problem of recognition of an identity in a scene. A system that automatically recognizes a face in a scene first detects it and normalizes it with respect to the pose, lighting and scale. Then, the system tries to associate the face to one or more faces stored in its database, and gives the set of faces that are considered as nearest to the detected face. This requires more computational resources and very robust algorithms for detection, normalization and recognition. In this paper we have implement different face recognition methods like Principle component analysis, Linear Discriminant Analysis and Fusion of PCA and LDA for face recognition. And better recognition rate is achieved by implementing neural network for classification.