With the outbreak of the COVID 19, the pandemic has prodded a vital improvement in the field of touch-less biometric scanning that can also examine a person is wearing a facial mask or not. The development of touch-less bio-metric technologies has gained importance due to COVID-19 hygiene requirements. Mask is a key element to avoid corona disease, but it affects the face recognition algorithms which loses the facial features when the face is covered by the mask. This paper proposes a method to recognize the face of a mask-wearing person using local binary pattern histogram (LBPH) algorithm from digital images. Also, develops a system that can detect instances where the face mask is on or not with the help of computer vision and convolutional neural network (CNN) architecture. Furthermore, this paper demonstrates a method of developing an attendance system using facial recognition. The proposed combination of face recognition and mask detection models is computationally efficient and easy to deploy on any embedded system (Raspberry Pi, Nano, Google Coral, Jetson, etc.) thus this paper showcases the proposed model implementation on Raspberry Pi 4 using an external web camera on live video and the result obtained is found to be satisfactory as a touch-less systems for validating the person’s biometric using face recognition and also checks whether the person wears the mask or not. These two factors of touch-less biometric along with confirmation of wearing face mask are mandatory requirements during pandemic periods like COVID-19.