Proceedings ArticleDOI
Self-similarity representation of Weber faces for kinship classification
Naman Kohli,Richa Singh,Mayank Vatsa +2 more
- pp 245-250
TLDR
A kinship classification algorithm that uses the local description of the pre-processed Weber face image to outperforms an existing algorithm and yields a classification accuracy of 75.2%.Abstract:
Establishing kinship using images can be utilized as context information in different applications including face recognition. However, the process of automatically detecting kinship in facial images is a challenging and relatively less explored task. The reason for this includes limited availability of datasets as well as the inherent variations amongst kins. This paper presents a kinship classification algorithm that uses the local description of the pre-processed Weber face image. A kinship database is also prepared that contains images pertaining to 272 kin pairs. The database includes images of celebrities (and their kins) and has four ethnicity groups and seven kinship groups. The proposed algorithm outperforms an existing algorithm and yields a classification accuracy of 75.2%.read more
Citations
More filters
Journal ArticleDOI
Learning deep compact similarity metric for kinship verification from face images
TL;DR: This work proposes a new kinship metric learning (KML) method with a coupled deep neural network (DNN) model, and introduces the property of hierarchical compactness into the coupled network to facilitate deep metric learning with limited amount of kinship training data.
Journal ArticleDOI
A Genetic Algorithm-Based Feature Selection for Kinship Verification
TL;DR: Local and global features were combined to describe the face images in a better way and the effective and discriminative features were selected using the kinship genetic algorithm and then fulfilled kinship verification.
Journal ArticleDOI
Video-based kinship verification using distance metric learning
Haibin Yan,Junlin Hu +1 more
TL;DR: A new video face dataset called Kinship Face Videos in the Wild (KFVW) is presented which were captured in wild conditions for the video-based kinship verification study, as well as the standard benchmark and experimental results show that metric learning based computational methods are not as good as that of human observers.
Journal ArticleDOI
Kinship verification from facial images by scalable similarity fusion
TL;DR: This paper proposes a scalable similarity learning (SSL) method for KVFI, which aims to learn a diagonal bilinear similarity model by online truncated gradient learning, which enjoys superiority in scalability and computational efficiency for practical KV FI with high-dimensional data.
Journal ArticleDOI
Learning a Multiple Kernel Similarity Metric for kinship verification
TL;DR: A novel Multiple Kernel Similarity Metric (MKSM) is proposed, in which, different from the Mahalanobis metric, the similarity computation is essentially based on an implicit nonlinear feature transformation.
References
More filters
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Book
Fundamentals of digital image processing
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Journal ArticleDOI
Face Description with Local Binary Patterns: Application to Face Recognition
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.