Feature Fusion and NRML Metric Learning for Facial Kinship Verification

• Published in: J.UCS (Annual print and CD-ROM archive ed.) 2023-01, Vol.29 (4), p.326-348
• Main Authors: Ramazankhani, Fahimeh, Yazdian-Dehkord, Mahdi, Rezaeian, Mehdi
• Format: Article
• Language: English
• Subjects: Feature extraction; feature fusion; kinship verification; Learning; metric learn; Support vector machines; Teaching methods; Verification
• ISSN: 0948-695X; 0948-6968
• DOI: 10.3897/jucs.89254

Features extracted from facial images are used in various fields such as kinship verification. The kinship verification system determines the kin or non-kin relation between a pair of facial images by analysing their facial features. In this research, different texture and color features have been used along with the metric learning method, to verify the kinship for the four kinship relations of father-son, father-daughter, mother-son and mother-daughter. First, by fusing effective features, NRML metric learning used to generate the discriminative feature vector, then SVM classifier used to verify to kinship relations. To measure the accuracy of the proposed method, KinFaceW-I and KinFaceW-II databases have been used. The results of the evaluations show that the feature fusion and NRML metric learning methods have been able to improve the performance of the kinship verification system. In addition to the proposed approach, the effect of feature extraction from the image blocks or the whole image is investigated and the results are presented. The results indicate that feature extraction in block form, can be effective in improving the final accuracy of kinship verification.