From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.Comment: Accepted for publication in Image and Vision Computing. Models and source code available at https://github.com/imatge-upc/sentiment-201

Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction

Frame-level visual features are generally aggregated in time with the techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust video-level representation. We here introduce a learnable aggregation technique whose primary objective is to retain short-time temporal structure between frame-level features and their spatial interdependencies in the representation. Also, it can be easily adapted to the cases where there have very scarce training samples. We evaluate the method on a real-fake expression prediction dataset to demonstrate its superiority. Our method obtains 65% score on the test dataset in the official MAP evaluation and there is only one misclassified decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) . Lastly, we believe that this method can be extended to different problems such as action/event recognition in future.Comment: Submitted to International Conference on Computer Vision Workshop

Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database