Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Automatic identity recognition from ear images represents an active field of research within
the biometric community. The ability to capture ear images from a distance and in a covert …

Machine-learning (ML) algorithms are increasingly utilized in privacy-sensitive applications
such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a …

Rapid progress in unconstrained face recognition has resulted in a saturation in recognition
accuracy for current benchmark datasets. While important for early progress, a chief …

This paper targets on the problem of set to set recognition, which learns the metric between
two image sets. Images in each set belong to the same identity. Since images in a set can be …

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The
network takes a face video or face image set of a person with a variable number of face …

Face recognition capabilities have recently made extraordinary leaps. Though this progress
is at least partially due to ballooning training set sizes–huge numbers of face images …

We propose a method to push the frontiers of unconstrained face recognition in the wild,
focusing on the problem of extreme pose variations. As opposed to current techniques which …

Anatomy education is a challenging but vital element in forming future medical
professionals. In this work, a personalized and interactive augmented reality system is …

This paper proposes to learn high-performance deep ConvNets with sparse neural
connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are …