A Face Recognition Method Using Deep Learning To Identify Mask And Unmask Objects

At the present, the use of face masks is growing day by day and it is mandated in most places across the world. People are encouraged to cover their faces when in public areas to avoid the spread of infection which can minimize the transmission of Covid-19 by 65 percent (according to the public health officials). So, it is important to detect people not wearing face masks. Additionally, face recognition has been applied to a wide area for security verification purposes since its performance, accuracy, and reliability [15] are better than any other traditional techniques like fingerprints, passwords, PINs, and so on. In recent years, facial recognition is becoming a challenging task because of various occlusions or masks like the existence of sunglasses, scarves, hats, and the use of make-up or disguise ingredients. So, the face recognition accuracy rate is affected by these types of masks. Moreover, the use of face masks has made conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, tracking school, and unlocking phones and laptops. As a result, we proposed a solution, Masked Facial Recognition (MFR) which can identify masked and unmasked people so individuals wearing a face mask do not need to take it out to authenticate themselves. We used the Deep Learning model, Inception ResNet V1 to train our model. The CASIA dataset [17] is applied for training images and the LFW (Labeled Faces in the Wild) dataset [18] with artificial marked faces are used for model evaluation purposes. The training and testing masked datasets are created using a Computer Vision-based approach (Dlib). We received an accuracy of around 96 percent for our three different trained models. As a result, the purposed work could be utilized effortlessly for both masked and unmasked face recognition and detection systems that are designed for safety and security verification purposes without any challenges

An Architecture For Real Time Face Recognition Using WMPCA

An architecture for real time face recognition using weighted modular principle component analysis (WMPCA) is presented in this paper. The WMPCA methodology splits the test face horizontally into sub-regions and analyzes each sub-region separately using PCA. The final decision is taken based on a weighted sum of the errors obtained from each region. This is based on assumption that different regions in a face vary at different rates with variations in expression and illumination. The WMPCA methodology has a better recognition rate, when compared with conventional PCA, for faces with large variations in expression and illumination. This methodology has a wide scope for parallelism. An architecture which exploits this parallelism is proposed in this paper. We also present a System On Programmable Chip (SOPC) implementation of face recognition system using this architecture. 1