Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios.

Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded.

A reasoning hardware platform for real-time common-sense inference.

Enabling Ambient Intelligence systems to understand the activities that are taking place in a supervised context is a rather complicated task. Moreover, this task cannot be successfully addressed while overlooking the mechanisms (common-sense knowledge and reasoning) that entitle us, as humans beings, to successfully undertake it. This work is based on the premise that Ambient Intelligence systems will be able to understand and react to context events if common-sense capabilities are embodied in them. However, there are some difficulties that need to be resolved before common-sense capabilities can be fully deployed to Ambient Intelligence. This work presents a hardware accelerated implementation of a common-sense knowledge-base system intended to improve response time and efficiency.