Face recognition has improved drastically in the past few decades after the advancement of machine learning in computer vision. However, the COVID-19 challenged the performance of the existing face recognition models badly as face recognition is expected to be performed while the persons are required to wear face mask to prevent the spread of the diseases. Even other communicable diseases pose the same problem. Identifying people from the images captured using surveillance cameras has become more challenging because of the poor quality of the images. The proposed model combines the attention module and MobileNet. The model uses the potential feature extraction abilities of the Covolutional Block Attention Module (CBAM). The relevant features from the masked, low-resolution face images are extracted by CBAM with MobileNet. When compared to existing low-resolution and masked face recognition models, our model shows a notable improvement in occluded low-resolution face recognition. The proposed method achieves an optimal trade-off between computational efficiency and recognition accuracy.