Facial Image Based Two-Level Model for Gender Classification

Abstract

Many scenes need facial gender identification with good accuracy. Deep convolutional neural networks (CNN) with large set of training data normally give good accuracy, however, to achieve good accuracy with uncertain training data is a difficult task due to their lower explanation and potential information loss. Moreover, the uncertain-ties resulted from illumination, postures and facial expressions can lead to low accuracy of the classification. In this paper, a shadowed sets based two-level model for gender classification is proposed to address the problem. Deep convolutional neural networks are used as one-level classifier. Combining the concept of shadowed sets, one-level classification results are divided into three parts: accept domain, reject domain and uncertain domain. Samples in the uncertain domain are selected as uncertain facial images for two-level reclassification. Results show that the proposed method can further improve the accuracy compared with several existing state-of-the-art methods on the LFW dataset and Adience dataset.

Keywords

• gender identification
• convolutional neural networks (cnn)
• shadowed sets
• uncertain domain