Toward Intelligent Ethnicity Recognition and Face Anonymization: An IncepX-Ensemble Model

Human recognition in the present globalized society involves various characteristics and cultural differentiations. Nevertheless, these categorizations, which encompass racial classification, raise questions over the ramifications for privacy and security. Given the emergence of facial recognition technology and growing apprehensions regarding privacy in the digital era, there is an urgent need for inventive strategies to tackle these intricate issues. This paper introduces the IncepX-Ensemble Model Approach, which is segregated into two main modules: ethnicity recognition and face anonymization. The ethnicity recognition module employs VGG16, ResNet-50, and MobileNet architectures with various YOLO variants for precise face detection, accurately classifying individuals based on ethnic background. Evaluation metrics include accuracy, precision, recall, and F1-score. The face anonymization module utilizes a hybrid model combining blurring, pixelization, and masking techniques to preserve privacy while obscuring identifiable facial attributes. Evaluation metrics for anonymization include Mean Average Precision and Frechet Inception Distance score. Experimental results demonstrate superior performance compared to previous models, advancing both ethnicity recognition and face anonymization in facial analysis while addressing privacy concerns.