Face-Age Modeling: A Pattern Recognition Analysis for Age Estimation

Oladipo, Oluwasegun; Osamor, Ifeoma Patricia; Osamor, Victor Chukwudi; Abiodun, Theresa Nkechi; Omoremi, Austin Olushola; Odim, Mba Obasi; Ekpo, Raphael Henshaw

doi:10.1109/bibm47256.2019.8983347

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…These are possible because of prevalent situation in Africa such as: Unclarified sworn to age affidavit that is not most-of -the-time confirmed, Birth certificate that was never collected at the point of giving birth to a child in the hospitals. Also, in sub-Saharan region of Africa, most people were not born in hospitals; as a result, most births were not documented by the National population commission [2].…”

Section: Introductionmentioning

confidence: 99%

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Face Age Estimation and the Other-race Effect

Oladipo¹,

Omidiora²,

Osamor³

2021

IJACSA

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Age estimation is an automated method of predicting human age from 2-D facial feature representations. The majority of studies carried out in this research area use the FG-NET and MORPH 2 databases to train and test developed systems, which are lacking in black-face content. Most age frauds are perpetuated in the sub-Saharan African region due to the unavailability of an official database and unregistered births in the rural areas. The issues of unverified age in the region made it possible for under-age voters, under-age drivers, and the engagement of over-aged sportsmen. The other-race effect could reduce the performance of face recognition techniques, which could make techniques that work for white faces underperform when deployed for use in the predominately black face region. This study examines the other-race effect on face-based age estimation by analyzing the accuracy of an age estimation system trained with predominantly black faces against the same age estimation system trained with predominately white faces. The developed age estimation system uses a genetic algorithmartificial neural network classifier and local binary pattern for texture and shape feature extraction. A total of 170 black faces were used for system testing. The result showed that the age estimation system trained with the predominantly black face database (GA-ANN-AES-855) outperformed the system trained with predominantly white faces (GA-ANN-AES-255) on testing with the aforementioned black face samples. The results obtained from the simulation were further subjected to inferential statistics, which established that the improvement in the correct classification rate was statistically significant. Hence, the otherrace effect affects face-based age estimation systems.

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Section: Introductionmentioning

confidence: 99%

“…In view of the above, most of the age claims made by the people in this region may not be true and are quite unreliable. Hence, there is a great need for an efficient and robust system for black-face age estimation and validation [2].…”

Section: Introductionmentioning

confidence: 99%

Face Age Estimation and the Other-race Effect

Oladipo¹,

Omidiora²,

Osamor³

2021

IJACSA

Self Cite

View full text Add to dashboard Cite

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Comparative analysis of features extraction techniques for black face age estimation

2022

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A computer-based age estimation is a technique that predicts an individual's age based on visual traits derived by analyzing a 2D picture of the individual's face. Age estimation is critical for access control, e-government, and effective human-computer interaction. The other-race effect has the potential to cause techniques designed for white faces to underperform when used in a region with black faces. The outcome is the consequence of intermittent training with faces of the same race and the encoding structure of the trained face images, which is based on the feature extraction technique used. This study contributes to a constructive comparison of three feature-extraction techniques, namely, local binary pattern (LBP), Gabor Wavelet (GW), and wavelet transformation, used in the development of a genetic algorithm (GA)artificial neural network (ANN)-based age estimation system. The feature extraction techniques used are proven to produce a wealth of shape and textural information. The GA-ANN constitutes the age classifier module. The correct classification rate was chosen as the performance metrics in this study. The results demonstrated that the LBP is a more robust representation of the black face than the GW and Wavelet transformations, as evidenced by its accuracy rate of 91.76 compared to 89.41 and 84.71 achieved with the GW and Wavelet transformation age estimation systems, respectively. This is a preview of subscription content, access via your institution.

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A novel genetic-artificial neural network based age estimation system

Oladipo

Omidiora

Osamor

2022

Sci Rep

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Age estimation is the ability to predict the age of an individual based on facial clues. This could be put to practical use in underage voting detection, underage driving detection, and overage sportsmen detection. To date, no popular automatic age estimation system has been developed to target black faces. This study developed a novel age estimation system from the combination of a genetic algorithm and a back propagation (BP)-trained artificial neural network (ANN) and using the local binary pattern feature extraction technique (LBGANN) targeted at black faces. The system was trained with a predominantly black face database, and the result was compared against that of a standard ANN system (LBANN). The results showed that the developed system LBGANN outperformed the LBANN in terms of the correct classification rate.

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Face-Age Modeling: A Pattern Recognition Analysis for Age Estimation

Cited by 4 publications

References 24 publications

Face Age Estimation and the Other-race Effect

Face Age Estimation and the Other-race Effect

Comparative analysis of features extraction techniques for black face age estimation

A novel genetic-artificial neural network based age estimation system

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