Abstract
Age and Gender classification of unconstrained imaging conditions has attracted an increased recognition as it is applicable in many real-world applications. Recent deep learning-based methods have shown encouraging performance in this field. We, therefore, propose an end-to-end deep learning-based method for robust age group and gender classification of unconstrained images. Particularly, we address the estimations problem with a classification based model that treats age value as a separate class and an independent label. The proposed deep convolutional neural network model learns the relevant informative age and gender representations directly from the image pixel. Technically, the model is first pre-trained on large-scale IMDb-WIKI facial aging dataset, and then fine-tuned on MORPH-II, another large-scale facial aging dataset to learn, and pick up the bias and particularities of each dataset. Finally, it is fine-tuned on the original dataset (OIU-Adience benchmark) with gender and age group labels. The experimental results when analyzed for estimation accuracy on OIU-Adience dataset, show that our model obtains the state-of-the-art performance in both age group and gender classification with an exact and one-off accuracy of 83.1% and 93.8% on age, and also an exact accuracy of 96.2% on gender.
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Agbo-Ajala, O., Viriri, S. (2019). Age Group and Gender Classification of Unconstrained Faces. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_32
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