Emotion-Infused Multimodal Biometric Fusion for Elevating User Authentication and Interaction

Abstract

This study combines many biometric techniques with emotions and provides a viable method for user engagement and authentication. The study uses recurrent neural networks, which contain LSTM (Long-Short-Term Memory) cells, while convolutional neural networks (CNN) are used for feature extraction. This work provides a standard extraction strategy that combines multiple biometric data sources, such as voice, fingerprints, irises, and faces. This allows the system to more easily\understand the user's emotional state temporal and geographical dimensions. After emotional expressions have changed over time, Using LSTM-based RNNs further improves the system. The overall accuracy and reliability of the emotion recognition model is improved because each biometric form has separate branches. Development of flexible models for advanced fusion techniques, their practical use, consideration of privacy concerns and continuous learning are possible directions for further development of this field. Overall, our work paves the way for safe, understandable, and compassionate technology by making significant advances\in user identification and engagement. The future offers many interesting features for biometric technology as well. Keywords: long-short-term memory (LSTM) cells, user authentication, interaction, biometric fusion with emotions, recurrent neural networks, convolutional neural networks, and multimodal biometric fusion.