Music Recommendation System based on Facial Emotion Analysis

Human facial emotion modeling and detection from facial images have proven to be a difficult problem for computer vision, primarily due to the fact that it relies heavily on large annotated datasets for training deep learning models. This paper introduces a new method that uses Deep Convolutional Generative Adversarial Networks (DCGANs) for unsupervised feature learning in order to facilitate strong supervised classification using significantly less labeled data. Our work addresses two particular objectives. Firstly, we generate different and realistic facial expression images representing different poses to enhance the variability of training data. Secondly, we utilize these deep features for the sophisticated analysis and classification of emotions and Action Units (AUs). Experimental evaluations on different databases indicate that our approach not only achieves encouraging performance in facial expression synthesis but also improves emotion and AU classification accuracy even with limited labeled samples. We also outline a new application where a GAN is used in a personalized song recommendation system, adapting to user preferences dynamically. This work sets the stage for more powerful and adaptable emotion recognition systems that have reduced dependence on data but wider scope of applications for GANs to multimedia personalization.