Learning discriminative features for face recognition

The paper is structured as follows. Section 2 de- scribes the proposed methods for the extraction of the facial soft biometric features. We propose a set of measures following

The experimental results, obtained on pub- licly available datasets of fingerprint, iris and 2-D face, show that the proposed method is highly competitive compared to

Training a Deep Neural Network (DNN), either in end-to-end fashion or to extract speaker embeddings, usually requires speaker labels for the background data, which is difficult

Assessing the results obtained for the mug shot against CCTV images experimental pro- tocol [KDG11], one may think that error should decrease when distance decreases since closer

As to the experiments, we have studied the effect of training and testing with images acquired at different dis- tances using two classical face recognition approaches (DCT-GMM-

Two different fusion architectures have been considered, an ideal case having exactly the same number of footstep and face signals, and a more realistic case with an adaptive fusion

Understanding how different facial regions from different color spaces are com- bined on a very challenging scenario has some remarkable benefits, for example: i) allowing

In the present work, an experimental framework for the extrac- tion of different facial regions of a face has been presented and used to understand their discrimination power.