Deeply learned representations are the state-of-the-art descriptors for face recognition
methods. These representations encode latent features that are difficult to explain …

Pearson product-moment correlation coefficient represents a fundamental tool for measuring
linear association between two data vectors. In various applications, it is often reasonable to …

Pearson product-moment correlation coefficient represents a fundamental measure of
similarity between two data vectors. In various applications, it is meaningful to consider its …

Deep Learning on face recognition problems has shown extremely high accuracy owing to
their ability in finding strongly discriminating features. However, face images in the wild …

In many real-world problems, there is typically a large discrepancy between the
characteristics of data used in training versus deployment. A prime example is the analysis …

Face recognition as a domain aims to generate salient representations of face images.
However, a face image has several facial attributes, such as eyeglasses, facial hair, etc. We …

In this paper, we propose an original CNN training strategy that brings together ideas from
both dropout-like regularization methods and solutions that learn discriminative features. We …

As representações aprendidas por redes profundas são os descritores estado-da-arte para
métodos de reconhecimento facial. Essas representações codificam características latentes …