Date of Publication :5th April 2025

Abstract: Abstract— Face Expression Recognition technology is transforming industries such as security, customized user experience, and healthcare. This article discusses major methodologies, such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and the Cross-Centroid Ripple Pattern (CRIP) technique. Although CNNs effectively handle image data and DNNs deal with complicated recognition tasks, the CRIP technique improves emotion detection by recognizing subtle facial expressions, which is especially useful in mental health evaluation. Challenges are still present, including high computational burden, data variation sensitivity, and issues in managing large facial feature differences. Ethical issues, bias, and system security also add to the complexity of implementation. Innovative solutions such as multimodal data fusion for enhanced accuracy, optimized algorithms for efficiency, and standardized frameworks providing fairness and security are needed to counter these challenges. This article examines these approaches, their applications in the real world, and the necessity of further research to make both efficiency and ethical deployment of facial recognition systems better.

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