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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. ...


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 ...


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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