@article {63259,
	title = {Towards large-scale face-based race classification on spark framework},
	journal = {Multimedia Tools and ApplicationsMultimedia Tools and Applications},
	volume = {78},
	year = {2019},
	month = {2019},
	pages = {26729{\textendash}26746},
	abstract = {Recently, the identification of an individual race has become an important research topic in face recognition systems, especially in large-scale face images. In this paper, we propose a new large-scale race classification method which combines Local Binary Pattern (LBP) and Logistic Regression (LR) on Spark framework. LBP is used to extract features from facial images, while spark{\textquoteright}s logistic regression is used as a classifier to improve the accuracy and speedup the classification system. The race recognition method is performed on Spark framework to process, in a parallel way, a large scale of data. The evaluation of our proposed method has been performed on two large face image datasets CAS-PEAL and Color FERET. Two major races were considered for this work, including Asian and Non-Asian races. As a result, we achieve the highest race classification accuracy (99.99\%) compared to Linear SVM, Naive Bayesian (NB), Random Forest(RF), and Decision Tree (DT) Spark{\textquoteright}s classifiers. Our method is compared against different state-of-the-art methods on race classification, the obtained results show that our approach is more efficient in terms of accuracy and processing time.},
	author = {Saliha, Mezzoudj and Ali, Behloul and Rachid, Seghir}
}
