Low-resolution face recognition (LR FR) aims to recognize faces from small size or poor
quality images with varying pose, illumination, expression, etc. It has received much …

Heterogeneous face recognition (HFR) refers to matching face imagery across different
domains. It has received much interest from the research community as a result of its …

Face images captured by surveillance cameras are often of low resolution (LR), which
adversely affects the performance of their matching with high-resolution (HR) gallery …

Although face recognition systems have achieved impressive performance in recent years,
the low-resolution face recognition task remains challenging, especially when the low …

We propose a novel coupled mappings method for low resolution face recognition using
deep convolutional neural networks (DCNNs). The proposed architecture consists of two …

In real world person re-identification (re-id), images of people captured at very different
resolutions from different locations need be matched. Existing re-id models typically …

Face recognition (FR) is one of the most extensively investigated problems in computer
vision. Significant progress in FR has been made due to the recent introduction of the larger …

Face images captured by surveillance videos usually have limited resolution. Due to
resolution mismatch, it is hard to match high-resolution (HR) faces with low-resolution (LR) …

Although deep learning techniques have largely improved face recognition, unconstrained
surveillance face recognition is still an unsolved challenge, due to the limited training data …

This paper proposes a new approach for Cross Modal Matching, ie the matching of patterns
represented in di erent modalities, when pairs of same/di erent data are available for training …