Age and gender recognition with random occluded data augmentation on facial images

2021 ◽  
Author(s):  
Chia-Yuan Hsu ◽  
Lu-En Lin ◽  
Chang Hong Lin
Keyword(s):  
Data Augmentation ◽  
Gender Recognition ◽  
Age And Gender ◽  
And Gender ◽  
Facial Images

Age and Gender Recognition from Speech Using Deep Neural Networks

2020 ◽  
pp. 332-344
Author(s):  
Héctor A. Sánchez-Hevia ◽  
Roberto Gil-Pita ◽  
Manuel Utrilla-Manso ◽  
Manuel Rosa-Zurera
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Gender Recognition ◽  
Age And Gender ◽  
And Gender

Geometric and Textural Cues for Automatic Kinship Verification

2015 ◽  
Vol 29 (03) ◽  
pp. 1556001 ◽  
Author(s):  
Andrea Bottino ◽  
Tiago Figueiredo Vieira ◽  
Ihtesham Ul Islam
Keyword(s):  
State Of The Art ◽  
Challenging Problem ◽  
Age And Gender ◽  
Kinship Verification ◽  
Verification Problem ◽  
Experimental Protocols ◽  
Textural Data ◽  
And Gender ◽  
Computer Identification ◽  
Facial Images

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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