Emotion Sensing Facial Recognition PDF

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Facial Emotion Recognition (FER) is the technology that analyses facial expressions from both static images and videos in order to reveal information on one's emotional state....

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Emotion Sensing Facial Recognition 1. What is the emerging dilemma all about? Technology has been utilized in a variety of functions, aimed to provide users with an enhanced quality of life, particularly with the utilization of softwares that senses a person's emotions through facial recognition, which is commonly called FER or facial emotion recognition (European Union, 2021). This particular software augments technology, allowing them to further analyze facial expressions from both static images and videos in order to reveal the user's emotional state. However, despite all these advantages, certain developments to technology may raise potential privacy risks, as users have no way of knowing how these images and videos are being processed. Through understanding facial recognition, these technologies provide users with enrichment and information, but its broad usage could potentially lead to the serious breach of privacy, leading up to a myriad of ethical concerns, similar to that of FER (Feng, Sadeh, & Zhang, 2021). Moreover, with the growing prominence of technological advancements, the demand for AI development has surged in popularity, leading to the management of these emotion sensors to artificial intelligence (Hava, 2018). This in turn contributes to privacy issues, as the captured images and videos are solely handled by these organizations; which may potentially be mishandled and disclosed if improperly handled, leading to the violation of a person’s right to privacy.

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What are the factors or events that led to this dilemma?

Some of the factors that led to Emotion-Sensing Facial Recognition, according to Algorithmia “Introduction to Facial Emotion Recognition”: Over 90 percent of human communication may be nonverbal, according to research; nevertheless, technology has failed to keep up, and traditional coding is often lousy at interpreting our intonations and intents. Nonetheless, emotion detection, also known as Affective Computing, is becoming more accessible to a wider range of developers. It will cover the fundamentals of judging emotion from data, as well as a few methods for you to practice emotion identification and running on your own. The ability to recognize and understand contextual emotion has wide-ranging implications for society and business. Governmental institutions might make effective use of the capacity to recognize emotions such as guilt, fear, and doubt in the public domain. Its ultimate goal is to make the world a safer environment for everyone. Companies have also taken use of emotion detection to improve the performance of their businesses. Disney intends to deploy face recognition technology for the impending release of Toy Story 5, in order to gauge the emotional reactions of the audience. Apple has even included a new function on the iPhone X called Animoji, which allows you to have an emoji that mimics your facial emotions created by a computer simulation. It's not unreasonable to expect that they will apply such skills in other applications in the near future.

Emotion Sensing Facial Recognition According to research, the United States Transportation Security Administration (TSA) started developing a novel surveillance system dubbed Screening of Passengers by Observation Techniques, or Spot, in 2003 to detect suspected terrorists by reading their facial expressions and behavior. Paul Ekman, a psychology professor at the University of California, San Francisco, developed a technique for recognizing and mapping minute facial expressions to emotions. This method was used to train "behavior detection cops" to check for signs of deception in people's faces. However, when it was first introduced in 2007, the software was beset with problems. Officers randomly referred passengers for interrogation, and the few arrests made were for reasons unrelated to terrorism. Even more worrisome, the program was used to justify racial profiling. Ekman tried to distance himself from Spot by claiming that his method was being misapplied. Others, on the other hand, believed that the program's demise was caused by an outof-date scientific concept that supports Ekman's method: that emotions can be objectively inferred via facial analysis. Using Ekman's method, technology companies have recently started to teach computers to recognize emotion via facial expressions. Some developers predict that artificial emotion detection technologies will not only beat humans in recognizing true emotions via facial expression analysis, but that these algorithms will also become sensitive to our innermost emotions, substantially boosting our involvement with our technology.

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What are the societal implications of this dilemma?

Emotion-sensing facial recognition raises problems as it uses artificial intelligence that makes society more cautious about their behaviors and software usage. When we utilize facial expressions to infer people's emotions, their most sensitive data is at stake. Emotion-sensing facial recognition is a disruptive technology that brings up significant concerns regarding its need and suitability for usage. Nonetheless, it has specific risks because identification is not the primary objective of this particular biometrics technology (Horvath & Vemou, 2021). As a consequence, users are worried that someone or a group of people are monitoring their faces or emotions while using the applications.

Emotion Sensing Facial Recognition 4. Why is it important to question the moral and ethical issues surrounding innovations in science and technology? It is important to question the moral and ethical issues surrounding innovations in science and technology since it provides us with the capacity to possess systematic knowledge of natural and human realities and to improve the conditions of our material life. Ethics helps us to identify moral values whose application improves our internal existence and balances our individual and social lives (Kardan, 2003). Ethics defines what is morally right or wrong for an individual and the society. Science and ethics are two necessary components that man uses to enjoy a good life and for their well-being, to realize their own essence, and to work toward perfection.

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In the face of this dilemma, why is it important to study STS?

During this age of modernization, rapidly we are transitioning into a world of fast-paced, advanced technology embedded with artificial intelligence such as our phones, tablets, televisions, and many more. By the knowledge and discipline from learning STS, we can be aware and be vigilant of the advantages and disadvantages of what science and technology can bring into society. Emotion AI, for example, has enormous potential in the marketing industry. It can enable companies to better customer experience not purely on rational intelligence. This technology can be helpful by learning from daily interactions, deciphering human emotional and cognitive communications, sensing desires and intentions, and understanding literal and nonliteral statements. In addition, there are also discussions regarding using Emotion AI to analyze a user's emotion based on their facial expressions and draw conclusions about the person’s mental health. (Affectiva, 2017). While Emotion AI has several social implications, it also raises multiple social and ethical issues, for example, problems in privacy. The goal here is not to eradicate emotion-capturing technologies, but to actually find appropriate ways to live and deal with it (McStay, 2016). Therefore, in the face of this dilemma instead of trying to invalidate an opposing situation, we must think and act carefully and critically to avoid exposing ourselves too much and be exploited for unwanted reasons.

Emotion Sensing Facial Recognition

References: Affectiva. (2018). SDK on the Spot: Suicide Prevention Project with Emotion Recognition. Retrieved January 31, 2022, from Affectiva.com website: https://blog.affectiva.com/sdk-on-thespot-suicide-prevention-project-with-emotion-recognition Bossen, K. (2020). Emotion AI – the future of artificial intelligence? DMEXCO. https://dmexco.com/stories/emotion-ai-the-artificial-emotional-intelligence/ Concerns and Attitudes Across Increasingly Diverse Deployment Scenarios. In Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021). European Union. (2021). TechDispatch #1/2021 - Facial Emotion Recognition. Retrievedfromhttps://edps.europa.eu/dataprotection/our- work/publications/techdispatch/techdisp atch-12021-facial-emotion-recognition_en Hava, C. (2018). Emotion-Sensing Technology – Friend or Foe?. Retrieved from https://www.hrmonline.com.au/technology/emotion-sensing-technology-friend-foe/ Introduction to Facial Emotion Recognition. (2018). https://algorithmia.com/blog/introduction-to-emotion-recognition

Algorithmia

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Kardan, M. A. (2003). The Conditions of Moral Education. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK208726/#:~:text=At%20the%20present%20time%2C %20ethical,conditions%20of%20our%20material%20life. McStay, A. (2016). THE RIGHT TO PRIVACY IN THE AGE OF EMOTIONAL AI. Schwartz, O. (2019). Don’t look now: why you should be worried about machines reading your emotions. The Guardian. https://www.theguardian.com/technology/2019/mar/06/facialrecognition-software-emotional-science Zhang, S., Feng, Y., & Sadeh, N. (2021). Facial Recognition: Understanding Privacy

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