<span lang="EN-US">During the coronavirus disease 2019 (COVID-19) pandemic, monitoring for wearing masks obtains a crucial attention due to the effect of wearing masks to prevent the spread of coronavirus. This work introduces two deep learning models, the former based on pre-trained convolutional neural network (CNN) which called MobileNetv2, and the latter is a new CNN architecture. These two models have been used to detect masked face with three classes (correct, not correct, and no mask). The experiments conducted on benchmark dataset which is face mask detection dataset from Kaggle. Moreover, the comparison between two models is driven to evaluate the results of these two proposed models.</span>

<p>The development of tracking and surveillance devices makes extracting useful information efficiently. Head tracking is an efficient method to obtain then analyze trajectory data and make a decision based on the spatiotemporal information of videos. Many applications are based on head tracking such as diseases some diagnosis,  the gestures languages, and drowsiness detection and so on. Abnormal head movement detection can be achieved using spatial information based on a single image (one frame) at a time without considering the temporal information over time. In this paper, a new method based on multi-images is proposed to track head in order to detect abnormal head movement depending on spatiotemporal Feature using Deep Neural Network DNN that employed the 3-Dimensional Convolution Neural Networks 3D CNN. The proposed method extracts the spatial information as well as the temporal information available in a video then analysis this information to make the decision based on time series (sequences of frames); these time series provides the tracking to the head overtime to make the decision. The new dataset created and gathered to implement with the proposed system and called Normal Abnormal Head Movement Dataset (NAHM) video dataset. The new dataset provides different subjects with different conditions that give more efficiency in the implementation of the proposed system. The accuracy of the training set that achieves by the proposed system reach to 88% and of validation set reaches to 86%. The values of loss function reach to 0.3 for the training set and 0.4 for the validation set.</p>

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