###### Abstract  
In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban security. This is a difficult task due to face occlusion, which makes it hard to capture effective features. Existing work focuses on improving performance while ignoring issues like a small dataset, high computational complexity, and a lack of lightweight and efficient feature descriptors. In this paper, face recognition (FR) using a Convolutional mixer (AFR-Conv) algorithm is developed to handle face occlusion problems. A novel AFR-Conv architecture is designed by assigning priority-based weight to the different face patches along with residual connections and an AdaBoost classifier for automatically recognizing human faces. The AFR-Conv also leverages the strengths of pre-trained CNNs by extracting features using ResNet-50, Inception-v3, and DenseNet-161. The AdaBoost classifier combines these features' weighted votes to predict labels for testing images. To develop this system, we use the data augmentation method to enhance the number of datasets using human face images. The AFR-Conv method is then used to extract robust features from images. Finally, to recognize human identity, an AdaBoost classifier is utilized. For the training and evaluation of the AFR-Conv model, a set of face images is collected from online data sources. The experimental results of the AFR-Conv approach are presented in terms of precision (PR), recall (RE), detection accuracy (DA), and F1-score metrics. Particularly, the proposed approach attains 95.5% PR, 97.6% RE, 97.5% DA, and 98.5% of F1-score on 8500 face images. The experimental results show that our proposed scheme outperforms advanced methods for face classification. intelligent systems; Internet of Things; COVID-19; face mask; computer vision; video analysis; face occlusion; deep learning; transfer learning; Convolutional Neural Network; ConvMixer model +
Footnote †: journal: Applied  
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balance between innovation and privacy protection is vital to ensuring these systems benefit society without compromising individual rights. However, traditional face detection and recognition algorithms require improved intelligence. Hence, there is an urgent need for modern face detection and recognition methods that effectively deal with occluded faces and result in higher detection and recognition accuracies. Masked Face Recognition (MFR) is a unique occlusion-based FR application. In contrast to regular occlusion FR, MFR has three significant challenges [2]. To begin with, there is a need for larger face datasets with masks. Second, masks completely occlude the mouth and nasal characteristics, reducing the effectiveness of facial feature extraction, so it is challenging to identify an individual when an object covers the face. At the same time, two unique instances are difficult to tackle using existing deep learning (DL) methods, such as face masks used for training and non-face covers for testing, and vice versa. Yet, under some unique circumstances, the two scenarios are critical. During the COVID-19 outbreak, for example, standard FR systems could not distinguish faces wearing masks. The authorities have many occluded images of suspects, but they do not have any clear face images. The concept of deep learning (DL) [3; 4; 5; 6; 7] is widely utilized in many applications. However, because of the long time required for network training, using DL in a real-time environment was challenging at first. Since then, the recent advances in DL approaches proposed in [4; 5; 6; 7] have motivated other authors to use the advancement of software and hardware in a parallel computing environment.