Abstract
According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.
Highlights
According to the CDC COVID Data tracker, over 30,000,000 cases have been confirmed since January 2020 in the US
From the Mordor Intelligence market report, the facial recognition market was valued at 3.72 billion USD in 2020 and is expected at a compound annual growth rate (CAGR) of 21.71% over the forecast period 2021 to 2026 [1]
We propose a camera-based Spartan Face Mask detection and Facial Recognition System consisting of four features: (1) mask detection, (2) mask position detection, (3) mask type detection, and (4) facial recognition with mask
Summary
According to the CDC COVID Data tracker, over 30,000,000 cases have been confirmed since January 2020 in the US. The novelty of this research is providing an end-to-end comprehensive system that supports face mask detection, mask position detection, mask type detection, and facial recognition with masks This Spartan system is built with five components which are training platform, server, supporting framework, hardware, and interface. For face recognition and facial feature extraction, many algorithms and deep learning models are proposed by other studies to detect faces with different kinds of occlusions. Song et al [9] found that traditional deep CNN networks performed poorly over occlusion face recognition problems They proposed a mask learning strategy based on a trunk CNN model trained for face recognition to find and discard feature elements from recognition. This work proposed four different models that solve the respecting features so that each model can be tuned separately to achieve best average accuracy and can be trained efficiently
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