Abstract: In this research, we present a novel approach to enhance the personalization of movie recommendations by incorporating age, gender, and emotion analysis. The proposed system utilizes deep learning models for age and gender prediction, along with emotion detection. We employ a YOLO based face analysis module for real-time face detection in images and video streams. The system then leverages these insights to recommend movies tailored to the user's demographic characteristics and emotional state. The age predictions are further refined into age ranges, providing a more user-friendly representation. The effectiveness of the recommendation system is demonstrated through comprehensive evaluations, achieving a high accuracy rate. The integration of age, gender, and emotion analysis adds a layer of personalization to movie recommendations, catering to the diverse preferences of users. This research contributes to the evolving field of recommendation systems, offering a more nuanced and individualized approach to movie suggestions.
Keywords: Movie Recommendation, Age Prediction, Gender Classification, Emotion Detection, Deep Learning
Cite:
Mr. M. Kishore Babu, Deevi Dharani Satya, Akula JyotheswarKumar, Budda prasanna kumar, Chilaka likith manoj,"Age, Gender and emotion-based movie recommendation using facial recognition", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13323.
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DOI:
10.17148/IJARCCE.2024.13323
