A hybrid deep learning CNN-ELM for age and gender classification

Automatic age and gender classification has been widely used in a large amount of applications, particularly in human-computer interaction, biometrics, visual surveillance, electronic customer, and commercial applications. In this paper, we introduce a hybrid structure which includes Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM), and integrates the synergy of two classifiers to deal with age and gender classification. The hybrid architecture makes the most of their advantages: CNN is used to extract the features from the input images while ELM classifies the intermediate results. We not only give the detailed deployment of our structure including design of parameters and layers, analysis of the hybrid architecture, and the derivation of back-propagation in this system during the iterations, but also adopt several measures to limit the risk of overfitting. After that, two popular datasets, such as, MORPH-II and Adience Benchmark, are used to verify our hybrid structure. Experimental results show that our hybrid architecture outperforms other studies on the same datasets by exhibiting significant performance improvement in terms of accuracy and efficiency.