Transfer learning and feature fusion for kinship verification

Dornaika, Fadi; Arganda‐Carreras, Ignacio; Serradilla, Oscar

doi:10.1007/s00521-019-04201-0

Cited by 19 publications

(12 citation statements)

References 42 publications

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“…Dornaika et al [ 20 ] proposed a novel framework that leverages deep facial features for kinship verification by incorporating three fusion levels. Their fusion levels first select the most relevant features, then it exploits a kinship-based multi-view metric learning method and finally merges classifiers’ responses.…”

Section: Related Workmentioning

confidence: 99%

“…The results of their work by combining features of different nature have achieved significantly higher accuracies than the human classification accuracies. Dornaika et al [20] proposed a novel framework that leverages deep facial features for kinship verification by incorporating three fusion levels. Their fusion levels first select the most relevant features, then it exploits a kinship-based multi-view metric learning method and finally merges classifiers' responses.…”

Section: Related Workmentioning

confidence: 99%

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A Study of Deep Learning-Based Face Recognition Models for Sibling Identification

Goel

Mehmood

Ugail

2021

Sensors

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Accurate identification of siblings through face recognition is a challenging task. This is predominantly because of the high degree of similarities among the faces of siblings. In this study, we investigate the use of state-of-the-art deep learning face recognition models to evaluate their capacity for discrimination between sibling faces using various similarity indices. The specific models examined for this purpose are FaceNet, VGGFace, VGG16, and VGG19. For each pair of images provided, the embeddings have been calculated using the chosen deep learning model. Five standard similarity measures, namely, cosine similarity, Euclidean distance, structured similarity, Manhattan distance, and Minkowski distance, are used to classify images looking for their identity on the threshold defined for each of the similarity measures. The accuracy, precision, and misclassification rate of each model are calculated using standard confusion matrices. Four different experimental datasets for full-frontal-face, eyes, nose, and forehead of sibling pairs are constructed using publicly available HQf subset of the SiblingDB database. The experimental results show that the accuracy of the chosen deep learning models to distinguish siblings based on the full-frontal-face and cropped face areas vary based on the face area compared. It is observed that VGGFace is best while comparing the full-frontal-face and eyes—the accuracy of classification being with more than 95% in this case. However, its accuracy degrades significantly when the noses are compared, while FaceNet provides the best result for classification based on the nose. Similarly, VGG16 and VGG19 are not the best models for classification using the eyes, but these models provide favorable results when foreheads are compared.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification

Goel

Mehmood

Ugail

2021

Sensors

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“… Dornaika, Arganda-Carreras & Serradilla, 2020 proposed an approach that extracted deep facial features for verifying kinship. A robust feature selection and discriminant projection of kinship-oriented data were incorporated into the structure.…”

Section: Literature Reviewmentioning

confidence: 99%

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Nader

El-Gamal

El–Sappagh

et al. 2021

PeerJ Computer Science

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Background and Objectives Kinship verification and recognition (KVR) is the machine’s ability to identify the genetic and blood relationship and its degree between humans’ facial images. The face is used because it is one of the most significant ways to recognize each other. Automatic KVR is an interesting area for investigation. It greatly affects real-world applications, such as searching for lost family members, forensics, and historical and genealogical studies. This paper presents a comprehensive survey that describes KVR applications and kinship types. It presents a literature review of current studies starting from handcrafted passing through shallow metric learning and ending with deep learning feature-based techniques. Furthermore, kinship mostly used datasets are discussed that in turn open the way for future directions for the research in this field. Also, the KVR limitations are discussed, such as insufficient illumination, noise, occlusion, and age variations problems. Finally, future research directions are presented, such as age and gender variation problems. Methods We applied a literature survey methodology to retrieve data from academic databases. An inclusion and exclusion criteria were set. Three stages were followed to select articles. Finally, the main KVR stages, along with the main methods in each stage, were presented. We believe that surveys can help researchers easily to detect areas that require more development and investigation. Results It was found that handcrafted, metric learning, and deep learning were widely utilized in kinship verification and recognition problem using facial images. Conclusions Despite the scientific efforts that aim to address this hot research topic, many future research areas require investigation, such as age and gender variation. In the end, the presented survey makes it easier for researchers to identify the new areas that require more investigation and research.

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“…kinship verification in our case) becomes more affordable when combined to the object deep features. As suggested in [7], we consider the object deep features for kinship verification. Facial and object features show a complementarity which extracted by MSIDA+WCCN method.…”

Section: A Extracting Multi-view Deep Featuresmentioning

confidence: 99%

“…The negative pairs and folds are predefined for the all four relations. For the facial deep features and object deep features, we extracted VGG-Face, VGG-F, VGG-M and VGG-S as this has shown to perform better than shallow methods [30], [7]. The tensor features are performs by the proposed MSIDA+WCCN method.…”

Section: A Experimental Setupmentioning

confidence: 99%

Multi-view Deep Features for Robust Facial Kinship Verification

Laiadi

Ouamane

Benakcha

et al. 2020

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)

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Automatic kinship verification from facial images is an emerging research topic in machine learning community. In this paper, we proposed an effective facial features extraction model based on multi-view deep features. Thus, we used four pre-trained deep learning models using eight features layers (FC6 and FC7 layers of each VGG-F, VGG-M, VGG-S and VGG-Face models) to train the proposed Multilinear Side-Information based Discriminant Analysis integrating Within Class Covariance Normalization (MSIDA+WCCN) method. Furthermore, we show that how can metric learning methods based on WCCN method integration improves the Simple Scoring Cosine similarity (SSC) method. We refer that we used the SSC method in RFIW'20 competition using the eight deep features concatenation. Thus, the integration of WCCN in the metric learning methods decreases the intra-class variations effect introduced by the deep features weights. We evaluate our proposed method on two kinship benchmarks namely KinFaceW-I and KinFaceW-II databases using four Parent-Child relations (Father-Son, Father-Daughter, Mother-Son and Mother-Daughter). Thus, the proposed MSIDA+WCCN method improves the SSC method with 12.80% and 14.65% on KinFaceW-I and KinFaceW-II databases, respectively. The results obtained are positively compared with some modern methods, including those that rely on deep learning.

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Transfer learning and feature fusion for kinship verification

Cited by 19 publications

References 42 publications

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Multi-view Deep Features for Robust Facial Kinship Verification

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