Automatic age estimation based on deep learning algorithm
Automatic age estimation has attracted much attention due to its potential applications. Most of the proposed approaches have mainly used low-level handcraft features to encode facial age related visual information and train an age estimation model. In this paper, we focus on age classification task in which face image is assigned to a label that represents an age range. We proposed a deep learning based framework for age classification task. In our proposed algorithm, Deep Convolutional Neural Networks (Deep ConvNets) are used to extract high-level complex age related visual features and predict age range of input face image. Due to lack of age labeled face images, we use the transfer learning strategy to train the Deep ConvNets. In addition, to describe the relationships between labels that compose an ordered sequence, we define a new loss function in the training process of age classification task. The experiments are conducted on a widely used age estimation dataset-Images of Groups of People. The experimental results demonstrate the excellent performance of our proposed algorithm against the state-of-the-art methods.
age estimation deep convolutional neural networks transfer learning loss function