CNN-based system for low resolution face recognition

Since the publication of the AlexNet in 2012, Deep Convolutional Neural Network models became the most promising and powerful technique for image representation. Specifically, the ability of their inner layers to extract high level abstractions of the input images, called deep features vectors, has been employed. Such vectors live in a high dimensional space in which an inner product and thus a metric is defined. The latter allows to carry out similarity measurements among them. This property is particularly useful in order to accomplish tasks such as Face Recognition. Indeed, in order to identify a person it is possible to compare deep features, used as face descriptors, from different identities by means of their similarities. Surveillance systems, among others, utilize this technique. To be precise, deep features extracted from probe images are matched against a database of descriptors from known identities. A critical point is that the database typically contains features extracted from high resolution images while the probes, taken by surveillance cameras, can be at a very low resolution. Therefore, it is mandatory to have a neural network which is able to extract deep features that are robust with respect to resolution variations. In this paper we discuss a CNN-based pipeline that we built for the task of Face Recognition among images with different resolution. The entire system relies on the ability of a CNN to extract deep features that can be used to perform a similarity search in order to fulfill the face recognition task.