Abstract The goal of Facial Kinship Verification (FKV) is to automatically determine whether two individuals have a kin relationship or not from their given facial images or videos. It is an emerging and challenging problem that has attracted increasing attention due to its practical applications. Over the past decade, significant progress has been achieved in this new field. Handcrafted features and deep learning techniques have been widely studied in FKV. The goal of this paper is to conduct a comprehensive review of the problem of FKV. We cover different aspects of the research, including problem definition, challenges, applications, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. In retrospect of what has been achieved so far, we identify gaps in current research and discuss potential future research directions. 

https://link.springer.com/content/pdf/10.1007/s11263-022-01605-9.pdf

Facial Kinship Verification: A Comprehensive Review and Outlook

Component-based metric learning for fully automatic kinship verification

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) framework for robust kinship verification. Particularly, an effective collaborative weighting strategy is constructed to explore the characteristics of cross-generation relations by corporately extracting features of both parents and children image pairs. Specifically, we take parents and children as a whole to extract the expressive local and non-local features. Different from the traditional works measuring similarity by distance, we interpolate the similarity calculations as the interior auxiliary weights into the deep CNN architecture to learn the whole and natural features. These similarity weights not only involve corresponding single points but also excavate the multiple relationships cross points, where local and non-local features are calculated by using these two kinds of distance measurements. Importantly, instead of separately conducting similarity computation and feature extraction, we integrate similarity learning and feature extraction into one unified learning process. The integrated representations deduced from local and non-local features can comprehensively express the informative semantics embedded in images and preserve abundant correlation knowledge from image pairs. Extensive experiments demonstrate the efficiency and superiority of the proposed model compared to some state-of-the-art kinship verification methods.

Kinship Verification Based on Cross-Generation Feature Interaction Learning

Personal identification based on face recognition is receiving extensive attention over the last few years in both research and real-time applications due to increasing emphasis on security. In this paper, Face Recognition using the fusion of bit-plane and binary image compression techniques are presented. Face images are resized to 256*256 to obtain the uniformity in the size of face images. The bit-plane compression algorithm is applied on the resized image to extract the features of the face. Similarly, the binary image compression technique is applied on 256*256 resized images to extract the features. Then the features produced from both the compression techniques are concatenated to obtain the final facial features. Finally, the test features and database features are compared using Euclidean Distance classifier. Extensive experiments are conducted on JAFFE, Indian database and L-speck face database. The true success rate of the proposed model based on the fusion of bitplane and binary image compression techniques provides better recognition rate than other existing state-of-the-art methods.

Face Recognition Based on the Fusion of Bit-plane and Binary Image Compression Techniques

In this paper, we propose a KinMix method to generate positive samples in the feature space for facial kinship verification. Unlike most existing data augmentation methods, we generate samples at the feature level instead of the original image level for data augmentation. Specifically, we use a pair of features with a kin relationship to construct a feature space instead of a single image, which aims to maintain the clustering results of features. We assume that a pair of kinship features lie in the same feature space, so that the generated feature remains the same kin relationship if it is sampled from the feature space. We use a linear sampling method to generate positive samples with the kin relationship. We evaluate our method on two widely used kinship datasets: KinFaceW-I and KinFaceW-II, and our experimental results are presented to show the effectiveness of the proposed approach.

KINMIX: A Data Augmentation Approach for Kinship Verification

Structure-aware fusion network for 3D scene understanding

In this paper, we propose a new single sample face recognition approach under the widely used sparse representation-based classification (SRC) framework. Previous work has shown that SRC only works well when there are sufficient number of training samples per person and not suitable for SSFR. To address this, we propose a domain transfer sparse representation-based classification (DT-SRC) method by using an auxiliary dataset to learn intra-class variations and transferring them into the single-sample training set. Since the auxiliary and training sets are likely captured in different environments, we apply the dictionary learning technique to learn a meta-space to transfer intra-class variations from the auxiliary set to the training set. To achieve this, we minimize the distribution difference of these two datasets in the meta-space so that such information can be effectively transferred. We extend DT-SRC to discriminative DT-SRC (DDTSRC) by making use of the label information samples in the auxiliary set to exploit more discriminative information in the learned meta-space. Experimental results on three face benchmark datasets demonstrate the effectiveness of the proposed approach.

Domain transfer sparse representation for single sample face recognition

In this paper, we investigate the problem of video-based kinship verification using discriminative metric learning. While several attempts have been made on facial kinship verification from still images, to our knowledge, the problem of video-based kinship verification has not been formally addressed in the literature. In this paper, we make the two contributions to video-based kinship verification. On one hand, we present a new video face dataset called Kinship Face Videos in the Wild (KFVW) which were captured in wild conditions for the video-based kinship verification study, as well as the standard benchmark. On the other hand, we employ two widely used metric learning methods for video-based kinship verification. Experimental results are presented to demonstrate the efficacy of our proposed dataset and the effectiveness of existing metric learning methods.

Haibin Yan

Papers

KINMIX: A Data Augmentation Approach for Kinship Verification

Structure-aware fusion network for 3D scene understanding

Domain transfer sparse representation for single sample face recognition

Discriminative metric learning for video-based kinship verification