Tensor cross-view quadratic discriminant analysis for kinship verification in the wild

Kinship recognition is a challenging problem with many practical applications. With much progress and milestones having been reached after ten years - we are now able to survey the research and create new milestones. We review the public resources and data challenges that enabled and inspired many to hone-in on the views of automatic kinship recognition in the visual domain. The different tasks are described in technical terms and syntax consistent across the problem domain and the practical value of each discussed and measured. State-of-the-art methods for visual kinship recognition problems, whether to discriminate between or generate from, are examined. As part of such, we review systems proposed as part of a recent data challenge held in conjunction with the 2020 IEEE Conference on Automatic Face and Gesture Recognition. We establish a stronghold for the state of progress for the different problems in a consistent manner. This survey will serve as the central resource for the work of the next decade to build upon. For the tenth anniversary, the demo code is provided for the various kin-based tasks. Detecting relatives with visual recognition and classifying the relationship is an area with high potential for impact in research and practice.

Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making.

The fault diagnosis of a marine turbocharger system is very crucial for realizing intelligent operation and maintenance in a big data analysis context. In order to improve the diagnostic rate of faults in engineering applications, in this paper, a new unsupervised machine learning algorithm, which is based on one-class support vector machine (OSVM), affinity propagation (AP) and Gaussian mixture model (GMM), called OAGFD is proposed for fault diagnosis. OSVM was firstly used to divide samples of marine turbocharger system into normal and fault samples, and only the fault samples are used in following steps to identify specific fault types. The AP was adopted automatically to provide an initial value for expectation maximization, which can obtain the maximum value of iteration parameters. The GMM is used to classify faults of marine turbocharger system and output the fault diagnosis results. Finally, the OAGFD is validated by actual data. The experiment results show that OAGFD can quickly and accurately be trained. The OAGFD method can achieve higher identification accuracy for multi-faults of marine turbocharger system and takes on faster operation speed and stronger generalization ability than tradition methods. It is an efficient and unsupervised fault diagnosis technique and has both theoretical and practical value. This research provides a new method for automatic fault diagnosis of the marine turbocharger system.

Fault Diagnosis of Marine Turbocharger System Based on an Unsupervised Algorithm

Kinship recognition is a challenging problem with many practical applications With much progress and milestones having been reached after ten years since pioneered - it is now that today we are able to survey their research and create new milestones We list and review the public resources and data challenges that enabled and inspired many to hone-in on one or more views of automatic kinship recognition in the visual domain The different tasks are described in technical terms and syntax consistent across the problem domain and the practical value of each discussed and measured State-of-the-art methods for visual kinship recognition problems, whether to discriminate between or generate from, are examined As part of such, we review systems proposed as part of a recent data challenge held in conjunction with the 2020 IEEE Conference on Automatic Face and Gesture Recognition We establish a stronghold for the state of progress for the different problems in a consistent manner We intend for this survey will serve as the central resource for work of the next decade to build upon For the tenth anniversary, demo code is provided for the various kin-based tasks Detecting relatives with visual recognition and classifying the relationship is an area with high potential for impact in research and practice

Visual Kinship Recognition: A Decade in the Making

Kinship, a soft biometric detectable in media, is fundamental for a myriad of use-cases. Despite the difficulty of detecting kinship, annual data challenges using still-images have consistently improved performances and attracted new researchers. Now, systems reach performance levels unforeseeable a decade ago, closing in on performances acceptable to deploy in practice. Similar to other biometric tasks, we expect systems can benefit from additional modalities. We hypothesize that adding modalities to FIW, which contains only still-images, will improve performance. Thus, to narrow the gap between research and reality and enhance the power of kinship recognition systems, we extend FIW with multimedia (MM) data (i.e., video, audio, and text captions). Specifically, we introduce the first publicly available multi-task MM kinship dataset. To build FIW MM, we developed machinery to automatically collect, annotate, and prepare the data, requiring minimal human input and no financial cost. The proposed MM corpus allows the problem statements to be more realistic template-based protocols. We show significant improvements in all benchmarks with the added modalities. The results highlight edge cases to inspire future research with different areas of improvement. FIW MM provides the data required to increase the potential of automated systems to detect kinship in MM. It also allows experts from diverse fields to collaborate in novel ways.

Families In Wild Multimedia (FIW-MM): A Multi-Modal Database for Recognizing Kinship

A literature survey on kinship verification through facial images

Kinship verification via facial images is a relatively new and challenging problem in computer vision. Prior studies in the literature have focused solely on gender-fixed kin relation, i.e., on the question of whether one gender-fixed kin relationship between given subjects can be established. In practice, however, large scale gender annotation is time-consuming and expensive. Instead, we propose in this paper to learn and predict with gender-unknown kin relations. To address this, we present a novel heterogeneous similarity learning (HSL) method. Motivated by the fact that different kinship relations may not only share some common genetic characteristics but also have its own inherited traits from parents to offspring, we aim to learn a similarity function under which the commonality among different kinship relations are captured and the geometry of each relation is preserved, simultaneously. We further derive a multi-view HSL method by optimal fusion of the similarity models from multiple feature representations, such that the complementary knowledge in multi-view kin data can be leveraged to obtain refined information. Experimental results demonstrate the effectiveness of our proposed methods.

Dakun Liu

Papers

A literature survey on kinship verification through facial images

Heterogeneous Similarity Learning for More Practical Kinship Verification

Social relationships classification using social contextual features and SVDD-based metric learning

New metric learning model using statistical inference for kinship verification

A novel factor analysis-based metric learning method for kinship verification