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Biometrics is based on the analysis of biometric features and is usually used for person recognition.
In this article we analyze biometric features related to touch screen gestures.
Among various approaches, gait analysis is commonly used to help identify the biometric features of PD.
Compared to uni-biometric systems, multi-biometric systems, which fuse multiple biometric features, can improve recognition accuracy and security.
The Convolutional Neural Network (CNN) has shown remarkable capability for learning biometric features that can offer robust and accurate matching.
The smartphones also provide an extra layer of security: Many biometric features will only work when used on the specific phone that belongs to the bank account holder.
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A set of soft-biometric features (called bag-of-soft-biometric features) are extracted from the person's images on the first step.
Moreover, the results show the huge potential of soft-biometric features.
Most of these works employ biometric or soft-biometric features to identify people.
A framework that automatically registers soft-biometric features every time that users log-in is proposed in [9].
Then, a set of soft-biometric features is extracted from each person, constituting the so-called bag-of-soft-biometrics.
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CEO of Professional Science Editing for Scientists @ prosciediting.com