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.
Highlights
A reasonable face recognition framework needs to work under various imaging conditions, for example, unique face presents, and distinctive illumination conditions
Face recognition is implemented by utilizing Histogram Oriented Gradients (HOG) features in AT & T dataset
The result obtained for both image datasets are compared with research in the same context using both histogram of Oriented Gradients (HOG) with support vector machine (SVM) or Naïve Bayesian (NB) classifiers
Summary
A reasonable face recognition framework needs to work under various imaging conditions, for example, unique face presents, and distinctive illumination conditions. The final descriptor is found by combining all block features in a window to construct the final HOG feature vector (Daniz et al, 2011, pp 1698-1603) The use of such method can have a drawback when used for face recognition as some of the produced features can be similar among multiple class. A person face may be blurry or undetectable that may affect the correctness of feature extraction process Another fact in face recognition system is the high dependability on feature extraction efficiency to work in a proper manner. For this fact, many scholars have attempt to overcome this problem by introducing various feature extraction techniques. The proposed method was tested using two face recognition system with SVM and NB classifiers
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