# Human Attribute Recognition--
## 1 Introduction
Typically, the linear Support Vector Machine (SVM) was used with different descriptors (such as ensemble of localized features, local binary patterns, color histograms, histogram of oriented gradients) to estimate the human attributes. However, as the correlation between human attributes were ignored in traditional methods, one single model was not suitable for estimating several attributes. For instance, descriptors suitable for gender recognition could not be effective enough to recognize the hairstyle. Therefore, conventional methods mostly focused on obtaining independent feature extractors for each attribute. After the advent of Convolutional Neural Networks (CNNs) and using it as a holistic feature extractor, a growing number of methods focused on models that can estimate multiple attributes at once. Earlier deep-based  
Figure 1: Typical pipeline to develop a HAR model. methods used shallow networks (e.g., 8-layer AlexNet [13]), while later models moved towards deeper architectures (e.g., residual network (ResNet)) [14]. The difficulties in HAR originates mainly due to the high-variability in human appearance particularly in intra-class samples. Nevertheless, the following factors have been identified as the basis for the development of robust HAR systems:  
* learn in an end-to-end manner and yield multiple attributes at once;
* extract a discriminative and comprehensive feature representation from the input data;
* leverage the intrinsic correlations between attributes;
* consider the location of each attribute in a weakly supervised manner;
* are robust to primary challenges such as low-resolution data, pose variation, occlusion, illumination variation, and cluttered background;
* handle the classes imbalance;
* manage the limited-data problem effectively. Despite the relevant advances and many research articles published, HAR can be considered still in its early stages. For the community to come up with original solutions, it is necessary to be aware of the history of advancements, state-of-the-art performance, and the existing datasets related to this field. Therefore, in this study, we discuss a collection of HAR related works, starting from the traditional one to the most recent proposals, and explain their possible advantages/drawbacks. We further analyze the performance of recent studies. Moreover, although we identified more than 15 publicly available HAR datasets, to the best of our knowledge, we do not have a clear discussion on the aspects that one should observe while collecting a HAR dataset. Thus, after taxonomizing the datasets and describing their main features and data collection setups, we discuss the critical issues of the data preparation step. Regarding the previously published surveys that addressed similar topics, we particularly mention [PERSON] et al. [15], where the facial attribute manipulation and estimation methods have been reviewed. However, to date, there is no solid survey on the recent advances in other sub-categories of human attribute analysis. As the essence of full-body, pedestrian, and cloth attribute recognition methods are similar to each other; in this paper, we cover all of them with a particular focus on the pedestrian attribute recognition methods. Meanwhile, Reference [16] is the only work similar to our survey that is about pedestrian attribute recognition. Several points distinguish our work from Reference [16]:  
* The recent literature on HAR has been mostly focused on addressing some particular challenges of this problem (such as class imbalance, attribute localization, etc.) rather devising a general HAR system.