Advancementsinmachinelearninghavebeenapplyingdeeply and widely in numerous fields. Especially, computer vision tasks for object detection in recent years have achieved great performances which are even better than human recognition ability. This work leverages machine learning methods and information systems to present a framework for student attendance combining machine learning-based face recognition algorithm and relational databases to store, recognize, and record student attendance. The proposed method is tested on various scenarios and is expected to apply in practical cases. We investigate the Histogram of Oriented Gra- dients (HOG) for face detection and to use cosine distance to recognize faces. The purpose of this study is face recognition in real-time i.e. using a webcam, camera of the mobile device, and from a photograph or from a set of faces tracked in a video. We measured the distance between the land- marks and compared the test image with different known encoded image landmarks in the recognition stage. Face Recognition includes extracting features and then recognizing it, in any case, such as brightness, transformations as translation, rotation, and scale image. We recognized that using the HOG algorithm to detect faces improves more and more efficient model and avoids time-consuming.