Enhanced kinship verification analysis based on color and texture handcrafted techniques

• Published in: The Visual computer Vol. 40; no. 4; pp. 2325 - 2346
• Main Authors: Nader, Nermeen, EL-Gamal, Fatma EL-Zahraa A., Elmogy, Mohammed
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Children & youth; Color; Computer Graphics; Computer Science; Computer vision; Datasets; Deep learning; Families & family life; Feature extraction; Gender; Image Processing and Computer Vision; Kinship; Original Article; Texture; Verification; Dense color histogram (DCH); Color correlogram (CC); Heterogeneous auto-similarities of characteristics (HASC); Kinship verification; Ensemble classifier; Color models
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-023-02919-6

Nowadays, kinship verification is an attractive research area within computer vision. It significantly affects applications in the real world, such as finding missing individuals and forensics. Despite the importance of this research topic, it still faces many challenges, such as low accuracy and illumination variations. Due to the existence of different classes of feature extraction techniques, different types of information can be extracted from the input data. Moreover, the fusion power produces complementary information that can address kinship verification problems. Therefore, this paper proposes a new approach for verifying kinship by fusing features from different perspectives, including color-texture and color features in different color spaces. Besides using promising methods in the field, such as local binary pattern (LBP) and scale-invariant feature transform (SIFT), the paper utilizes other feature extraction methods, which are heterogeneous auto-similarities of characteristics (HASC), color correlogram (CC), and dense color histogram (DCH). As far as we know, these features haven’t been employed before in this research area. Accordingly, the proposed approach goes into six stages: preprocessing, feature extraction, feature normalization, feature fusion, feature representation, and kinship verification. The proposed approach was evaluated on the KinFaceW-I and KinFaceW-II field standard datasets, achieving maximum accuracy of 79.54% and 90.65%, respectively. Compared with many state-of-the-art approaches, the results of the proposed approach reflect the promising achievements and encourage the authors to plan for future enhancement.