Two phased histogram of oriented gradient feature selection strategy for face recognition

Daroon Mudhafar Hamad; Moayad Yousif Potrus

doi:10.25212/lfu.qzj.5.4.31

Authors

Daroon Mudhafar Hamad Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq
Moayad Yousif Potrus Department of Software Engineering, College of Engineering, Salahaddin University, Erbil, Kurdistan Region, Iraq

DOI:

https://doi.org/10.25212/lfu.qzj.5.4.31

Keywords:

Feature Selection, Histogram of Oriented Gradient, Face Recognition.

Abstract

Face recognition system, as any recognition process, depends highly on features extracted from face images. The selected features play a great role in deciding the recognition rate result. In this paper, a two-phase feature extraction and selection process is used for face recognition system. The process depends on histogram of Oriented Gradients (HOG) feature extraction and window size use to determine similarity between classes. Low number of features are used (big window size) to divide classes into small closed-similarity groups as first recognition phase. Then, the best matched class is found using larger number of features where differences between classes are bigger. The proposed method was applied to Essex face dataset using support vector machine (SVM) and Naïve Bayesian (NB) methods for comparison. The proposed method achieved 5% and 10% better recognition rate compared to SVM and NB respectively.

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