Improving the Recognition of Faces using LBP and SVM Optimized by PSO Technique

  Nisha,  Maitreyee Dutta

A face is the distinctive feature of the person providing an identity in the society. Face recognition is an important and challenging technique used for two primary tasks that is identification (or recognition) and verification (or authentication) purposes. There are various challenges in the field of face recognition like variations of illumination, pose, aging, identity, hairstyle, facial expressions etc. This research aims to develop a method to increase the efficiency of recognition using Particle Swarm Optimization (PSO) technique. In this paper, two feature extraction algorithms namely Principal Component Analysis (PCA) and Local Binary Pattern (LBP) techniques are used to extract features from images. The Local Binary Pattern (LBP) has been proved to be effective for image representation. In the recognition process, we used Support Vector Machine (SVM) for classification combined with Particle Swarm Optimization. The classifier performance and the length of selected feature vector are considered for performance evaluation using the Faces94 database. From the experimental results, it is observed that the proposed method could increase the recognition accuracy rate.

Keywords- Face Recognition, Kernel Functions, Local Binary Pattern (LBP), Principal Component Analysis (PCA), Support Vector Machine (SVM), Particle Swarm Optimization (PSO).

Unique Identification Number - IJEDR1704045
Page Number(s) - 297-303
Pubished in - Volume 5 | Issue 4 | October 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Nisha,  Maitreyee Dutta,   "Improving the Recognition of Faces using LBP and SVM Optimized by PSO Technique", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 4, pp.297-303, October 2017, Available at :http://www.ijedr.org/papers/IJEDR1704045.pdf