Facial attribute recognition is one of the most popular research topics in computer vision and pattern recognition, and has great research significance of analyzing and understanding facial images. At the same time, it has a wide range of practical application value in many fields such as image retrieval, face recognition, micro-expression recognition and recommendation system. With the rapid development of deep learning, a large number of deep learning based facial attribute recognition (termed DFAR) methods have been put forward by domestic and foreign scholars. First the overall process of the facial attribute recognition method is described. Then, according to the different mechanisms of model construction, the part-based and holistic DFAR methods are reviewed and discussed in detail, respectively. Specifically, the part-based DFAR methods are classified according to whether or not to adopt the regular area localization technique, while the holistic DFAR methods are distinguished from the perspectives of single-task learning and multi-task learning, where multi-task learning based DFAR methods are further subdivided according to whether the attribute grouping strategy is used. Next, several popular databases and evaluation metrics on facial attribute recognition are introduced, and the performance of the state-of-the-art DFAR methods is compared and analyzed. Finally, the future research directions of the DFAR methods are provided.