Wearing masks contributed to slowing the spread of the coronavirus disease (COVID-19) as the World Health Organization (WHO) recommended wearing face masks especially with the spreading of virus variants like omicron. Although people accept the idea of wearing these masks, it is still unknown the effect of covering parts of the face on social interaction among people in general and children in particular. Moreover, Social isolation affects emotional moods, which causes stress, sadness, and depression. In the current study, we have been exploring the emotional inferences on faces with and without a mask. The system can pick up the universal emotions: fear, disgust, anger, surprise, contempt, sadness, and happiness. The researchers in deep learning are concerned with global pandemic COVID-19 to enhance public health service. The proposed model is developed with a machine learning algorithm through the Haar feature-based cascade classifiers. The built model can detect people's emotions with mask and without a mask with high accuracy.

Finger vein verification has recently gained the attention of many researchers as one of the most interesting biometrics. This paper proposes a deep learning model called the Deep Fingers Vein Learning (DFVL). to improve recognition accuracy by training a Convolutional Neural Network. The final model consists of the following layers: three convolutional & ReLU, pooling, fully connected, soft-max and classification. All this after the hand image goes through the basic stages of determining the region of interest by operations within the preprocessing. The effect of changing parameter values was examined, analyzed, and discussed. The best accuracy results recorded by tuning the network parameter is 81.7%. This percentage was increased to 89% after using the five-finger fusion (Correct match of three or more fingers).