Most of the current metric learning methods are proposed for point-to-point distance (PPD)
based classification. In many computer vision tasks, however, we need to measure the point …

The main challenge in recognizing faces in video is effectively exploiting the multiple frames
of a face and the accompanying dynamic signature. One prominent method is based on …

In this paper, we focus on face clustering in videos. To promote the performance of video
clustering by multiple intrinsic cues, ie, pairwise constraints and multiple views, we propose …

Canonical correlation analysis (CCA) is a main technique of linear subspace approach for
two-view dimension reduction by finding basis vectors with maximum correlation between …

Domain adaptation leverages rich knowledge from a related source domain so that it can be
used to perform tasks in a target domain. For more knowledge to be obtained under relaxed …

Due to advances in sensor, storage, and networking technologies, data is being generated
on a daily basis at an ever-increasing pace in a wide range of applications, including cloud …

Multi-view data with each view corresponding to a type of feature set are common in real
world. Usually, previous multi-view learning methods assume complete views. However …

The attention paid to matrix manifolds has grown considerably in the computer vision
community in recent years. There are a wide range of important applications including face …

Face recognition on large-scale video in the wild is becoming increasingly important due to
the ubiquity of video data captured by surveillance cameras, handheld devices, Internet …

In this paper, we address the problem of classifying image sets for face recognition, where
each set contains images belonging to the same subject and typically covering large …