access icon free Weighted similarity and distance metric learning for unconstrained face verification with 3D frontalisation

In this study, the authors focus on the challenging problem of verifying faces captured under unconstrained conditions. Unconstrained face images often vary largely in poses, illuminations, expressions, occlusions, and ages. To address these challenges, they combine face frontalisation method with metric learning. To deal with the variations of poses, they apply an improved 3D face frontalisation method to generate the frontal view of the face images. Recent studies observed that bilinear similarity and Mahalanobis distance have a promising performance on measuring the similarity of two images. Based on these studies, they propose a weighted similarity and distance metric learning method which balances the role of bilinear similarity and Mahalanobis distance to better measure the similarity of an image pair. All the experiments are conducted based on the labelled faces in the wild database, and the experimental results show the effectiveness of their method.

Inspec keywords: face recognition; learning (artificial intelligence)

Other keywords: frontal view; unconstrained face images; distance metric learning method; recent studies; Mahalanobis distance; poses; weighted similarity; bilinear similarity; image pair; unconstrained conditions; unconstrained face verification; authors focus; labelled faces

Subjects: Image recognition; Computer vision and image processing techniques

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6327
Loading

Related content

content/journals/10.1049/iet-ipr.2018.6327
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading