###### Abstract
Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), Restricted Boltzmann Machines (RBM), Recursive Neural Networks (RNN), and Stack-Based Auto-Encoders (SAE) are examples of deep learning architectures that are effectively used in a variety of applications such as natural language processing (NLP), bioinformatics, and computer vision [8; 9]. Deep learning-based techniques have benefited face detection, recognition, and forecasting applications [10; 11]. Several existing FAR systems used the classic computer vision techniques highlighted in Section 2. Regarding their feature extraction approaches, these strategies have limitations. All of these works use feature extractors that were handcrafted. As a result, the extracted image features reflect the variations between the natural and occluded face images in the spatial and frequency domains. In a few research studies, we noticed that deep learning (DL) methods are used to construct FRS systems. These FRS systems used AlexNet and VGG networks in previous DL-based ways to extract features and recognize human images. The AlexNet model, on the other hand, has only eight layers, making it a shallow model. As a result of facial occlusion, extracting robust characteristics from human face images could be more effective. Furthermore, achieving reasonable accuracy is time-consuming and needs to be more efficient for instantaneous FRS applications. Compared with the newer DL model, the VGG network model has a vanishing gradient problem and is also extremely slow. The current DL-based Convolutional Neural Network (CNN) model includes many parameters and calculations requiring complex hardware. A learning-based technique called a ConvMixer was recently introduced that uses image patches along with a convolution-based architecture instead of a transformer-based architecture. The authors use depthwise convolution followed by pointwise convolution as their main ConvMixer layer, which is repeated a number of times according to the model depth. In practice, the ConvMixer architecture gives better validation accuracy than a basic CNN model with four times fewer parameters. The ConvMixer has been successfully used to extract features from image-based recognition systems. These ConvMixer models outperform several earlier transfer learning (TL) models and classic handcrafted feature extraction methods. Accordingly, this paper proposes a new FAR method for FR systems based on a residual connection in the ConvMixer architecture with AdaBoost. This study provides a novel technique for dealing with challenges caused by facial occlusions and variations in facial expression. These limitations exist in recognizing human faces [10; 11]. From past studies, it noticed that it is very difficult to recognize human faces automatically due to mask. This paper proposes a deep learning methodology (ENSEMBLE-FRO) for video analysis and surveillance systems, especially in partially occluded environments. The proposed ENSEMBLE-FRO comprises three pre-trained DL architectures: ResNeXt-50, Inception-v3, and DenseNet-161. Using an augmentation method, the authors create a synthetic face mask evaluation dataset using many prominent public verification datasets, including LFW, CALFW, CPLFW, and CFP. The Real-World Masked Face Dataset (RMFD) is used in addition to the synthesized versions of typical FR testing datasets. Several performance metrics are used to assess the performance of the proposed technique, such as precision, recall, accuracy, and F1-score.