emotion detection

How Can AI Detect Human Emotions Accurately? - Latest, Trending Automation News


The ability for Artificial Intelligence to make decisions based upon large datasets is largely changing the way organizations operate in today's world. Each roadmap, each goal and decision has to be supported by a certain statistic or justification as to why the organization is headed in that direction. This is another reason why Data Engineers and Scientists are heavily paid throughout the world for they have the job to analyze and make predictions, which can potentially put the company's future at stake. However, that's not all that AI can do. Utilizing Computer Vision and Facial Recognition capabilities, AI is able to detect human emotions accurately.

policy.ai – CSET


More: Emotion Detection AI Is a $20 Billion Industry. New Research Says It Can't Do What It Claims China's Algorithms of Repression More: Emotion Detection AI Is a $20 Billion Industry. New Research Says It Can't Do What It Claims China's Algorithms of Repression

The Future of Facial Recognition - Smartmicros


Face recognition is one of the applications of Deep Learning which has witnessed successful practical implementations within a short span of time. From secure transactions to facial detection, the future of facial recognition looks bright and we're currently at the beginning of the facial recognition revolution. Currently, the applications focus moreover strengthening the security systems but its potential applications can bring a change to many other services. Cloud computing, edge processing, and Artificial Intelligence have played a crucial role in the advancement of computer systems. Here, we will be listing out some prime applications and impact of facial recognition.

Do not laugh -- A simple AI powered game using TensorFlow.js and Electron


If you previously played around a bit with AI or machine learning code chances are you have already seen a library called TensorFlow in action. TensorFlow is Google's open source AI framework for machine learning (ML) and it is widely used for tasks like image classification -- Just what we need for our little game. The only drawbacks are, it is a Python library and it can only use nVidia GPUs for acceleration. Yes, you absolutely want that GPU acceleration when doing anything ML related. Luckily the TensorFlow team has ported the library to JavaScript and released TensorFlow.js (tfjs) which lets us use it inside an Electron app.

Jointly Learning to Detect Emotions and Predict Facebook Reactions

arXiv.org Machine Learning

The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Vision to Natural Language Processing. In this paper we focus on Facebook posts paired with "reactions" of multiple users, and we investigate their relationships with classes of emotions that are typically considered in the task of emotion detection. We are inspired by the idea of introducing a connection between reactions and emotions by means of First-Order Logic formulas, and we propose an end-to-end neural model that is able to jointly learn to detect emotions and predict Facebook reactions in a multi-task environment, where the logic formulas are converted into polynomial constraints. Our model is trained using a large collection of unsupervised texts together with data labeled with emotion classes and Facebook posts that include reactions. An extended experimental analysis that leverages a large collection of Facebook posts shows that the tasks of emotion classification and reaction prediction can both benefit from their interaction.

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

arXiv.org Artificial Intelligence

Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.

Amazon Says It Can Detect Fear on Your Face. You Scared?


Amazon announced a breakthrough from its AI experts Monday: Their algorithms can now read fear on your face, at a cost of $0.001 per image--or less if you process more than 1 million images. The news sparked interest because Amazon is at the center of a political tussle over the accuracy and regulation of facial recognition. Amazon sells a facial-recognition service, part of a suite of image-analysis features called Rekognition, to customers that include police departments. Another Rekognition service tries to discern the gender of faces in photos. The company said Monday that the gender feature had been improved--apparently a response to research showing it was much less accurate for people with darker skin.

End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker

arXiv.org Machine Learning

Automatic detection of emotion has the potential to revolutionize mental health and wellbeing. Recent work has been successful in predicting affect from unimodal electrocardiogram (ECG) data. However, to be immediately relevant for real-world applications, physiology-based emotion detection must make use of ubiquitous photoplethysmogram (PPG) data collected by affordable consumer fitness trackers. Additionally, applications of emotion detection in healthcare settings will require some measure of uncertainty over model predictions. We present here a Bayesian deep learning model for end-to-end classification of emotional valence, using only the unimodal heartbeat time series collected by a consumer fitness tracker (Garmin V\'ivosmart 3). We collected a new dataset for this task, and report a peak F1 score of 0.7. This demonstrates a practical relevance of physiology-based emotion detection `in the wild' today.



This is a post related to my recent talk at PyLadies Vancouver. The talk is about how to use the EmoPy toolkit in Linux Ubuntu 16.04 with OpenCV Python to perform Emotion detection in images and videos. You can find my slides here. EmoPy is an open-source emotion detection toolkit developed by Thoughtworks and currently supports OS X. However, it has not been tested on a Linux OS.

Deep Learning based Emotion Recognition System Using Speech Features and Transcriptions

arXiv.org Machine Learning

This paper proposes a speech emotion recognition method based on speech features and speech transcriptions (text). Speech features such as Spectrogram and Mel-frequency Cepstral Coefficients (MFCC) help retain emotionrelated low-level characteristics in speech whereas text helps capture semantic meaning, both of which help in different aspects of emotion detection. We experimented with several Deep Neural Network (DNN) architectures, which take in different combinations of speech features and text as inputs. The proposed network architectures achieve higher accuracies when compared to state-of-the-art methods on a benchmark dataset. The combined MFCC-Text Convolutional Neural Network (CNN) model proved to be the most accurate in recognizing emotions in IEMOCAP data. We achieved an almost 7% increase in overall accuracy as well as an improvement of 5.6% in average class accuracy when compared to existing state-of-the-art methods.