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Regression approach has been used in image denoising as well as face recognition area.
By demonstrating specific apps such as face recognition, a scene played out with the house welcoming him to the stage.
Applications such as face recognition, spam filtering, and recommendation engines which use large dataset uses machine learning.
Neural networks are widely used for image processing applications such as face recognition and face detection [22, 37, 44].
The designed wavelets may be potentially useful in applications such as face recognition, image segmentation and texture analysis.
With the success of smartphones, mobile developers are becoming more ingenious in creating sophisticated applications to attract users such as face recognition, interactive gaming, and augmented reality applications.
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On tests, the four super-recognizers performed at least two standard deviations above the mean, which meant that they were "about as good at face recognition and perception as developmental prosopagnosics are bad".
The super-recognizers, Russell et al. write, "are about as good at face recognition and perception as [lifelong] prosopagnosics are bad"—that is, about two or three standard deviations above average, while the most severe prosopagnosics have face-recognizing abilities two or three standard deviations below average.
The super-recognizers, Russell et al. write, "are about as good at face recognition and perception as [lifelong] prosopagnosics are bad" — that is, about two or three standard deviations above average, while the most severe prosopagnosics have face-recognizing abilities two or three standard deviations below average.
Thus, these two lines of evidence support the assumption that individuals with AS have face recognition difficulties.
In the future, as face-recognition technology becomes more accurate, it will become even more intrusive, because of pressures to expand the biometric database.
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