• convolutional neural network
• deep learning
• face recognition
• riconoscimento facciale

The availability of large training datasets and the introduction of GP-GPUs, along with a number of algorithmic news, have fostered the recent progress in the computer vision field of Artificial Intelligence.

The latest state-of-the-art approaches for face recognition have taken advantage of such a progress by exploiting deep convolutional neural networks (DCNNs). I have examined these methods, as well as the most widely used face datasets and the techniques for face detection and alignment.

Moreover, I have selected a promising method to be developed, based on the training of DCNNs through a ''triplet loss function''.
Triplets are composed by three images: an anchor sample, a positive sample similar to the anchor, and a negative sample that instead belongs to a different class.
The investigated loss function enables the network to learn a mapping that projects the similar input images to embeddings whose Euclidean distance is smaller enough than the distance between the embeddings associated to anchor and negative samples.
In this manner, after training on triplets of face images, the similarity verification between two face samples can be reduced to the comparison of the Euclidean distance of their embeddings to a distance threshold.

I have implemented the software tools needed to realize such a technique and I have trained and tested a number of models with publicly accessible face datasets, assessing different training settings and exploring the hyper-parameters space.

Finally, I have built a face recognition system that enhances the face verification accuracy of the base method, even on strongly unaligned face images, and also surpasses the human accuracy.