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Simply put, Euclidean distance measures how far away two items are (see Neil Slater's comment). In order to apply this to a pattern recognition task, you will need to convert the items to compare (in your case images of faces) into feature vectors (ie lists of numerical values), and then you do a pairwise comparison to work out how distant two faces are. ...


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You can try using a multi-input model. Here is a recent post with a similar discussion, with the required architecture defined in the answer. Instead of combining the separate models, you can create a model which uses image and numerical data side by side. Keras allows you to use different types of data using multi input structure via functional API. And ...


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There is some research on this topic. See, for example, the papers Robot Identification and Localization with Pointing Gestures (2018) and Proximity Human-Robot Interaction Using Pointing Gestures and a Wrist-mounted IMU (2019), by Boris Gromov et al., where the human is assumed to possess an inertial measurement unit (IMU) attached to the arm


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