Related papers: Kinship Representation Learning with Face Componen…

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this research, first, a human study is conducted to understand the capabilities of human mind and…

Kinship verification aims to identify the kin relation between two given face images. It is a very challenging problem due to the lack of training data and facial similarity variations between kinship pairs. In this work, we build a novel…

Early methods used face representations in kinship verification, which are less accurate than joint representations of parents' and children's facial images learned from scratch. We propose an approach featuring graph neural network…

Facial Kinship Verification is the task of determining the degree of familial relationship between two facial images. It has recently gained a lot of interest in various applications spanning forensic science, social media, and demographic…

Facial Kinship Verification (FKV) aims at automatically determining whether two subjects have a kinship relation based on human faces. It has potential applications in finding missing children and social media analysis. Traditional FKV…

Kinship verification and kinship retrieval are emerging tasks in computer vision. Kinship verification aims at determining whether two facial images are from related people or not, while kinship retrieval is the task of retrieving possible…

Kinship verification from facial images has been recognized as an emerging yet challenging technique in many potential computer vision applications. In this paper, we propose a novel cross-generation feature interaction learning (CFIL)…

Kinship verification using facial photographs captured in the wild is difficult area of research in the science of computer vision. It might be used for a variety of applications, including image annotation and searching for missing…

Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical…

We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from uncropped face…

We propose a new face recognition method, called a pairwise relational network (PRN), which takes local appearance features around landmark points on the feature map, and captures unique pairwise relations with the same identity and…

Existing face recognition using deep neural networks is difficult to know what kind of features are used to discriminate the identities of face images clearly. To investigate the effective features for face recognition, we propose a novel…

Computational facial models that capture properties of facial cues related to aging and kinship increasingly attract the attention of the research community, enabling the development of reliable methods for age progression, age estimation,…

Image colorization methods have shown prominent performance on natural images. However, since humans are more sensitive to faces, existing methods are insufficient to meet the demands when applied to facial images, typically showing…

Visual kinship recognition aims to identify blood relatives from facial images. Its practical application-- like in law-enforcement, video surveillance, automatic family album management, and more-- has motivated many researchers to put…

Approaches for kinship verification often rely on cosine distances between face identification features. However, due to gender bias inherent in these features, it is hard to reliably predict whether two opposite-gender pairs are related.…

Kinship verification is a long-standing research challenge in computer vision. The visual differences presented to the face have a significant effect on the recognition capabilities of the kinship systems. We argue that aggregating multiple…

Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from presentation attacks. Most existing FAS methods capture various cues (e.g., texture, depth and reflection) to distinguish the live faces from the…

Robust face detection is one of the most important pre-processing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D facial models, etc. Although this topic has been intensely…

Face images are one of the main areas of focus for computer vision, receiving on a wide variety of tasks. Although face recognition is probably the most widely researched, many other tasks such as kinship detection, facial expression…