{"id":12035,"date":"2020-04-20T10:48:27","date_gmt":"2020-04-20T14:48:27","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/affectiva-building-ai-that-can-read-human-emotions\/"},"modified":"2020-04-20T10:48:27","modified_gmt":"2020-04-20T14:48:27","slug":"affectiva-building-ai-that-reads-human-emotions","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/affectiva-building-ai-that-reads-human-emotions\/","title":{"rendered":"Affectiva: building AI that reads human emotions"},"content":{"rendered":"

As the amount of data we collect increases, companies are leveraging advanced analytics to drive insights also from the most unusual sources of data, including people facial expressions or speech. In fact, an interesting application of artificial intelligence (AI) and machine learning (ML) lies in the space of emotions. Emotion AI is an emotion detection technology, that \u201ccombines analysis of both face and speech as complementary signals to provide richer insight into the human expression of <\/em>emotion<\/em>\u201d. [1]<\/a> Affectiva, a Boston-based company founded in 2009 as a spin-off from the MIT Media Lab, is a pioneer in this space.[2]<\/a><\/p>\n

By using a standard webcam, Affectiva can identify a face and its key landmarks (e.g. mouth corners, nose tip etc.), to classify facial expressions into seven main emotions (anger, contempt, disgust, fear, joy, sadness and surprise). With pre-recorded audio, speech detection can be integrated as well, with the ability to classify \u201chow\u201d something is said, with a frequency of few hundred millisecond. With about six million faces analyzed in 87 Countries, data accuracy is in the high 90th<\/sup> percentile.<\/p>\n