This is one of the trending artificial intelligence project ideas. This project seeks to expand on a pioneering modern application of Deep Learning – facial emotion recognition. Although facial emotion recognition has long been the subject of research and study, it is only now that we are witnessing tangible results of that analysis.
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The Deep Learning facial emotion detection and recognition system are designed to identify and interpret human facial expressions. It can detect the core human emotions in real-time, including happy, sad, angry, afraid, surprise, disgust, and neutral. First, the automatic facial expression recognition system will detect the facial expressions from a cluttered scene to perform facial feature extraction and facial expression classification.
Then, it will enforce a Convolution Neural Network (CNN) for training a dataset (FER2013). This dataset contains seven facial features – happy, sad, surprise, fear, anger, disgust, and neutral. The unique aspect of this facial emotion detection and recognition system is that it can monitor human emotions, discriminate between good and bad emotions, and label them appropriately. It can also use the tagged emotion information to identify the thinking patterns and behavior of a person.
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What is Facial Emotion Recognition? Facial Emotion Recognition is a technology used for analysing sentiments by different sources, such as pictures and videos. It belongs to the family of technologies often referred to as ‘affective computing’, a multidisciplinary field of research on computer’s capabilities to recognise and interpret human emotions and affective states and it often builds on Artificial Intelligence technologies. Facial expressions are forms of non-verbal communication, providing hints for human emotions. For decades, decoding such emotion expressions has been a research interest in the field of psychology (Ekman and Friesen 2003; Lang et al. 1993) but also to the Human Computer Interaction field (Cowie et al. 2001; Abdat et al. 2011). Recently, the high diffusion of cameras and the technological advances in biometrics analysis, machine learning and pattern recognition have played a prominent role in the development of the FER technology. Many companies, ranging from tech giants such as NEC or Google to smaller ones, such as Affectiva or Eyeris invest in the technology, which shows its growing importance. There are also several EU research and innovation program Horizon2020 initiatives1 exploring the use of the technology.
What are the data protection issues?
Due to its use of biometric data and Artificial Intelligence technologies, FER shares some of the risks of using facial recognition and artificial intelligence. Nevertheless, this technology carries also its own specific risks. Being a biometrics technology, where aiming at identification does not appear as a primary goal, risks related to emotion interpretation accuracy and its application are eminent.
Provision of personalised services • analyse emotions to display personalised messages in smart environments • provide personalised recommendations e.g. on music selection or cultural material • analyse facial expressions to predict individual reaction to movies Customer behaviour analysis and advertising • analyse customers’ emotions while shopping focused on either goods or their arrangement within the shop • advertising signage at a railway station using a system of recognition and facial tracking for marketing purposes Healthcare • detect autism or neurodegenerative diseases • predict psychotic disorders or depression to identify users in need of assistance • suicide prevention • detect depression in elderly people • observe patients conditions during treatment Employment • help decision-making of recruiters • identify uninterested candidates in a job interview • monitor moods and attention of employees
Education • monitor students’ attention • detect emotional reaction of users to an educative program and adapt the learning path • design affective tutoring system • detect engagement in online learning Public safety • lie detectors and smart border control • predictive screening of public spaces to identify emotions triggering potential terrorism threat • analysing footage from crime scenes to indicate potential motives in a crime Crime detection • detect and reduce fraudulent insurance claims • deploy fraud prevention strategies • spot shoplifters Other • driver fatigue detection • detection of political attitudes.
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