# Human Attribute Recognition--
## 6 Discussion  
### Discussion Over HAR Datasets  
In recent years, HAR has received much interest from the scientific community, with a relatively large number of datasets developed for this purpose; this is also demonstrated by the number of citations. We performed a query for each HAR related database on the Google Scholar (scholar.google,com) search engine, and extracted its corresponding number of citations; the results are graphically presented in Figure 3. In the past decade, more than 15 databases related to this research field have been published, and most of them received hundreds of citations. In Table 1, we chose to taxonomize the attributes semantically into demographic attributes (gender, age, ethnicity), appearance attributes (related to the appearance of the subject, such as hairstyle, hair color, weight, etc. ), accessory information (which indicate the presence of a certain accessory, such as a hat, handbag, backpack etc.) and clothing attributes (which describe the garments worn by the subjects). In total, we have described 17 datasets, the majority containing over ten thousand images. These datasets can be seen as a continuous effort made by researchers to provide large amounts of varied data required by the latest deep learning neural networks. 1. Attributes definition. The first issues that should be addressed when developing a new dataset for HAR are: (1) _which attributes should be annotated?_ and (2) _how many and which classes are required to describe an attribute properly?_. Obviously, both these questions depend on the application domain of the HAR system. Generally, the ultimate goal on a HAR, regardless of the application domain, would be to accurately describe an image in terms of human-understandable semantic labels, for example, \"a five-year-old boy, dressed in blue jeans, with a yellow T-shirt carrying a striped backpack\". As for the second question, the answer is straightforward for some attributes, such as gender, but it becomes more complex and subjective for other attributes, such as age or clothing information. Let's take for example, the age label; different datasets provided different classes for this information: PETA distinguishes between _AgeLess15_, _Age16-30_, _Age31-45_, _Age46-60_, _AgeAbove61_, while CRP dataset adopted a different age classification scheme: _child, teen, young adult, middle aged_ and _senior_. Now, if a HAR analyzer is integrated into a surveillance system in a crowded environment, such as Disneyland, and this system should be used to locate a missing child, the age labels from the PETA dataset are not detailed enough, as the \"lowest\" age class is AgeLess15. Secondly, these differences between the different taxonomies make it difficult to assess the performance of a newly developed algorithm across different datasets. 2. Unbalanced data. An important issue in any dataset is related to unbalanced data. Although some datasets were developed by explicitly striking for balanced classes, some classes are not that  
Figure 3: Number of citations to HAR datasets. The datasets are arranged in an increasing order by their publication date. The “oldest” dataset being HAT, published in 2009, while the latest is RAP v2, published in 2018.  
frequent (especially those related to clothing information), and fully balanced datasets are not a trivial problem. The problem of imbalance also affects the demographic attributes. In all HAR datasets, the class of young children is poorly represented. To illustrate the problem of unbalanced classes, we selected two of the most prominent HAR related datatsets which are labeled with age information: CRP and PETA. In Figure 4, for each of these two datasets, we plot a pie charts to show age distribution of the labeled images. Furthermore, as datasets are usually gathered in a single region (city, country, continent), the data tends to be unbalanced in terms of ethnicity.