Automatic Kinship verification aims at recognizing the degree of kinship of two individuals from their facial images and it has possible applications in image retrieval and annotation, forensics and historical studies. This is a recent and challenging problem, which must deal with different degrees of kinship and variations in age and gender. Our work explores the computer identification of parent-child pairs using a combination of (i) features of different natures, based on geometric and textural data, (ii) feature selection and (iii) state-of-the-art classifiers. Experiments show that the proposed approach provides a valuable solution to the kinship verification problem, as suggested by its comparison with different methods on the same data and the same experimental protocols. We further show the good generalization capabilities of our method in several cross-database experiments.

Geometric and Textural Cues for Automatic Kinship Verification / Bottino, ANDREA GIUSEPPE; FIGUEIREDO VIEIRA, Tiago; UL-ISLAM, Ihtesham. - In: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE. - ISSN 0218-0014. - STAMPA. - 29:3(2015), pp. 1-18. [10.1142/S0218001415560017]

Geometric and Textural Cues for Automatic Kinship Verification

BOTTINO, ANDREA GIUSEPPE;FIGUEIREDO VIEIRA, TIAGO;UL-ISLAM, IHTESHAM
2015

Abstract

Automatic Kinship verification aims at recognizing the degree of kinship of two individuals from their facial images and it has possible applications in image retrieval and annotation, forensics and historical studies. This is a recent and challenging problem, which must deal with different degrees of kinship and variations in age and gender. Our work explores the computer identification of parent-child pairs using a combination of (i) features of different natures, based on geometric and textural data, (ii) feature selection and (iii) state-of-the-art classifiers. Experiments show that the proposed approach provides a valuable solution to the kinship verification problem, as suggested by its comparison with different methods on the same data and the same experimental protocols. We further show the good generalization capabilities of our method in several cross-database experiments.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2582541