Rana el Kaliouby is a co-founder of Affectiva, a company which dedicates to emotion measurement technology. Rana was one of the research scientists at MIT media lab before. In this talk, she demonstrated how her team brings emotions to our digital life.
Many years ago, Rana was a computer scientist in Egypt. Because she wanted to pursue a Ph.D. degree in England, she had to leave her family. When Rana talked with her family through laptop, she felt that the majority part of her emotions was disappeared. We all know that emotions are the most significant part of human's face, it help us expresses our feeling. Therefore, this experience led Rana rethink the way how human interacts with electronic devices. She realized that our electronic devices are good at handling information, but their shortcoming is reading human's facial expressions. To close this gap, Rana came up with a technology that enables electronic devices recognizing facial expressions of how someone is feeling.
Rana said it is difficult to read computers reading human facial expressions. She tried to train computer by feeding faces of people from different cultures, ages and genders. Through machine learning, computer is able to identify how different parts of face change shape in response to different emotions. Now, this facial recognizing technology is ready and she demonstrated it in this TED talk. Her demo showed us how powerful this technology is, this technology could even detect tiny changes of human face such as subtle smile, poker face and smirk.
Rana believed this technology could eventually become a critical component of many electronic devices because it adds a new sense for those devices. In the near future, electronic devices embedded this technology can actively detect user's reaction through facial expressions and take correspondent action. For example, cars could detect whether driver is tired or not. A learning app could also detect whether students are confused or bored and adjusted the progress. Moreover, a refrigerator could also auto-lock itself to prevent people from overeating because of stress. There are so many applications for this technology, so Rana and her team decided to make this technology available to software developers around the globe. Programmers are invited to join her community to create innovative applications. At the end, Roan said she was aware of the potential risk this technology might has, but she believed the cons can not offset the pros brought by this technology.
Many years ago, Rana was a computer scientist in Egypt. Because she wanted to pursue a Ph.D. degree in England, she had to leave her family. When Rana talked with her family through laptop, she felt that the majority part of her emotions was disappeared. We all know that emotions are the most significant part of human's face, it help us expresses our feeling. Therefore, this experience led Rana rethink the way how human interacts with electronic devices. She realized that our electronic devices are good at handling information, but their shortcoming is reading human's facial expressions. To close this gap, Rana came up with a technology that enables electronic devices recognizing facial expressions of how someone is feeling.
Rana said it is difficult to read computers reading human facial expressions. She tried to train computer by feeding faces of people from different cultures, ages and genders. Through machine learning, computer is able to identify how different parts of face change shape in response to different emotions. Now, this facial recognizing technology is ready and she demonstrated it in this TED talk. Her demo showed us how powerful this technology is, this technology could even detect tiny changes of human face such as subtle smile, poker face and smirk.
Rana believed this technology could eventually become a critical component of many electronic devices because it adds a new sense for those devices. In the near future, electronic devices embedded this technology can actively detect user's reaction through facial expressions and take correspondent action. For example, cars could detect whether driver is tired or not. A learning app could also detect whether students are confused or bored and adjusted the progress. Moreover, a refrigerator could also auto-lock itself to prevent people from overeating because of stress. There are so many applications for this technology, so Rana and her team decided to make this technology available to software developers around the globe. Programmers are invited to join her community to create innovative applications. At the end, Roan said she was aware of the potential risk this technology might has, but she believed the cons can not offset the pros brought by this technology.
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