Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces

المؤلفون المشاركون

Viriri, Serestina
Agbo-Ajala, Olatunbosun

المصدر

The Scientific World Journal

العدد

المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2020-04-21

دولة النشر

مصر

عدد الصفحات

12

التخصصات الرئيسية

الطب البشري
تكنولوجيا المعلومات وعلم الحاسوب

الملخص EN

Age and gender predictions of unfiltered faces classify unconstrained real-world facial images into predefined age and gender.

Significant improvements have been made in this research area due to its usefulness in intelligent real-world applications.

However, the traditional methods on the unfiltered benchmarks show their incompetency to handle large degrees of variations in those unconstrained images.

More recently, Convolutional Neural Networks (CNNs) based methods have been extensively used for the classification task due to their excellent performance in facial analysis.

In this work, we propose a novel end-to-end CNN approach, to achieve robust age group and gender classification of unfiltered real-world faces.

The two-level CNN architecture includes feature extraction and classification itself.

The feature extraction extracts feature corresponding to age and gender, while the classification classifies the face images to the correct age group and gender.

Particularly, we address the large variations in the unfiltered real-world faces with a robust image preprocessing algorithm that prepares and processes those faces before being fed into the CNN model.

Technically, our network is pretrained on an IMDb-WIKI with noisy labels and then fine-tuned on MORPH-II and finally on the training set of the OIU-Adience (original) dataset.

The experimental results, when analyzed for classification accuracy on the same OIU-Adience benchmark, show that our model obtains the state-of-the-art performance in both age group and gender classification.

It improves over the best-reported results by 16.6% (exact accuracy) and 3.2% (one-off accuracy) for age group classification and also there is an improvement of 3.0% (exact accuracy) for gender classification.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Agbo-Ajala, Olatunbosun& Viriri, Serestina. 2020. Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces. The Scientific World Journal،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213782

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Agbo-Ajala, Olatunbosun& Viriri, Serestina. Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces. The Scientific World Journal No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1213782

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Agbo-Ajala, Olatunbosun& Viriri, Serestina. Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces. The Scientific World Journal. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213782

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1213782