Face recognition with a hyperbolic metric classification model

Facial recognition systems are increasingly being used in smartphones as biometric security instead of passwords, or in airports as automated electronic passport control. It is also emerging in other forms of technology, for example in robotics. This creates large collections of photos that cannot be managed. Data mining tools and machine learning methods can be used to process these datasets and use them for prediction and classification. In such algorithms, using the most suitable distance metrics to define similarities among data has a crucial role. This paper investigates the usage of the Poincaré metric, which is used primarily in hyperbolic geometry, on the well-known k Nearest Neighbours classification algorithm. We applied this method to a database of face images. Our results indicate that the Poincaré metric is helpful with the Large Margin Nearest Neighbour learning (LMNN) method tested on an image dataset. We found that for small values of k, up to five, the algorithm using the Poincaré metric with the Edge filter gave the best results.