Determining Age and Emotion
[From Step right up, let the computer look at your face and tell you your age and Physorg.com]
A press release from the University of Illinois focuses on smart cameras that can identify the approximate age of a person. The age recognition algorithms can estimate ages from 1 year to 93 years. The software’s accuracy ranges from about 50 percent when estimating ages to within 5 years, to more than 80 percent when estimating ages to within 10 years. The software consists of three parts: face detection, discriminative manifold learning, and multiple linear regression. The software was trained on a database of 1,600 faces, but a larger database can add more accuracy. The team was led by Thomas Huang.
The article lists a number of possible applications for this technique:
For example, age-recognition algorithms could stop underage drinkers from entering bars, prevent minors from purchasing tobacco products from vending machines, and deny children access to adult Web sites . . . . In addition to performing tasks such as security control and surveillance monitoring, age-estimation software also could be used for electronic customer relationship management. For example, a camera snapping photos of customers could collect demographic data – such as how many adult men and women buy burgers, or what percentage of teenagers purchase a particular soft drink. Or, combined with algorithms that identify a person’s sex, age-estimation software could help target specific audiences for specific advertisements. For example, a store display might advertise a new automobile or boat as a man walks by, or new clothing or cosmetics as a woman walks by.
Another research project mentioned at Physorg.com recognized six emotion states based on audio and visual data. The system could recognize happiness, sadness, anger, fear, surprise, and disgust across cultures with a success rate of 82%.
The researchers’ system extracted a large number of vocal characteristics, such as “prosodic features,” which include the rhythm, intensity, rate, and frequency of speech. Facial features were extracted holistically. Then, the researchers trained the system on several short video samples of individuals showing different emotions, from which it connected certain features with emotions.
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