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’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’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.

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’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’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.

In this work, we propose a secure version of ElGamal public key cryptosystem, and prove that it is semantically secure assuming the hardness of what we call the two-dimensional decisional Diffie-Hellman (2DDDH) problem, this cryptosystem is distinguished by the speed of encryption and decryption processes and by its resistance to active adversaries. Since the 2DDDH problem is harder than the decisional Diffie-Hellman (DDH) problem (as it will be seen), one may conclude that our model reinforces the exchange security compared to the existed cryptosystems falling within the same context, also we discuss the difficult problems that guarantee its security.

In this work, we propose a secure version of ElGamal public key cryptosystem, and prove that it is semantically secure assuming the hardness of what we call the two-dimensional decisional Diffie-Hellman (2DDDH) problem, this cryptosystem is distinguished by the speed of encryption and decryption processes and by its resistance to active adversaries. Since the 2DDDH problem is harder than the decisional Diffie-Hellman (DDH) problem (as it will be seen), one may conclude that our model reinforces the exchange security compared to the existed cryptosystems falling within the same context, also we discuss the difficult problems that guarantee its security.