Age prediction is an active study field that can substantially affect many computer vision problems like object recognition. In this paper, an accurate system with extensive experiments is proposed to provide an efficient and accurate approach for age range prediction of people from their facial images. Histogram Equalization technique is used to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases, and image resizing is used to unify all image sizes. Moreover, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images. Then, Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) are used for the classification processes. To evaluate the performance of the proposed system, both Leave-One-Out (LOO) and Confusion Matrix (CM) are used. The extensive and intensified experiments improved the age range predicting performance up to 98.6%.

Optimistic concurrency control is widely studied in the literature due to the properties of non-blocking and deadlock free execution especially in the domain of real-time systems. In this paper we review the substantial research of optimistic concurrency control protocols. We characterize them into four categories and explore their properties. Then we investigate the general concepts and properties related to Optimistic concurrency control Finally, we demonstrate a comparison table between the varieties of these

protocols.