LAUSR

Age estimation using face images is an exciting and challenging task. The traits from the face images are used to determine age, gender, ethnic background, and emotion of people. Among this set of traits, age estimation can be valuable in several potential real-time applications. The traditional hand-crafted methods relied-on for age estimation, cannot correctly estimate the age. The availability of huge datasets for training and an increase in computational power has made deep learning with convolutional neural network a better method for age estimation; convolutional neural network will learn discriminative feature descriptors directly from image pixels. Several convolutional neural net work approaches have been proposed by many of the researchers, and these have made a significant impact on the results and performances of age estimation systems. In this paper, we present a thorough study of the state-of-the-art deep learning techniques which estimate age from human faces. We discuss the popular convolutional neural network architectures used for age estimation, presents a critical analysis of the performance of some deep learning models on popular facial aging datasets, and study the standard evaluation metrics used for performance evaluations. Finally, we try to analyze the main aspects that can increase the performance of the age estimation system in future.