CSA-ECNet: Automated root cause analysis using channel and spatial attention-based explainable convolutional network in face recognition

Face Recognition is the process of identifying people by extracting their facial features, and it is widely utilized in several applications, including authentication, healthcare, and security. The traditional approaches faced troubles in providing better accuracy and computational efficiency due to the lack of identifying the facial patterns. Therefore, a Root Cause Analysis (RCA) is essential in a face recognition system to prevent failures in recognizing faces. Hence, the Channel and Spatial Attention-based Explainable Convolutional Network (CSA-ECNet) model is proposed to enhance the face recognition results through detecting the defects and analyzing the root causes. The incorporation of an explainable technique helps to provide insights to the CSA-ECNet model in detecting the root causes, thereby increasing the performance of the CSA-ECNet model in recognizing faces without any failures. The incorporation of the Channel and Spatial Attention (CSA) facilitates increasing the accuracy by enabling the CSA-ECNet model to selectively concentrate on the vital spatial regions and feature channels, which strengthens the model’s ability to handle various aspects, including poor lighting. Experimental results demonstrate the exceptional performance of the CSA-ECNet model, reporting the high sensitivity of 97.79%, specificity of 98.44%, and accuracy of 98.11% for 90% of training data on Face Recognition Dataset.