Abstract
Age and gender prediction from facial images with high accuracy is of immense importance in various fields viz. social media, retail business, and academic research. In this paper, we make age prediction with an optimized model for efficient training by wide residual networks (ResNet) and efficient gradient optimization on loss function for better test accuracy. Our model has been evaluated and tested on IMDb-WIKI and APPA-REAL dataset and it performed well in evaluation compared to traditional deep Convolutional Neural Networks such as VGG-16, naive SVM classifiers with feature edge parameters, etc. The final accuracy achieved for our model is 96.269% with the wide ResNet architecture along with augmentation and erasing techniques on images. Significant reduction in the Mean Apparent Error (1.73) on apparent images and Mean Apparent Error (1.65) on Real images of the APPA-REAL dataset has been achieved with respect to traditional VGG-16 model.
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Debgupta, R., Chaudhuri, B.B., Tripathy, B.K. (2020). A Wide ResNet-Based Approach for Age and Gender Estimation in Face Images. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_44
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DOI: https://doi.org/10.1007/978-981-15-1286-5_44
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