# Human Attribute Recognition--
## 6 Discussion
### Discussion Over HAR Datasets
This is an important issue as some studies [172] proved the existence of _the other race effect_ --the tendency to more easily recognize faces from the same ethnicity-- for machine learning classifier. 3. Data context. Strongly linked to the problem of data unbalance is the context or environment in which the frames were captured. The environment has a great influence on the distribution of the clothing and demographic (age, gender) attributes. In [75] the authors noticed \"strong correlations between image event and the frequent human attributes in it\". This is quite logical, as one would expect to encounter more casual outfits in a picnic or sporting event, while at ceremonies (wedding, graduation proms), people tend to be more elegant and dressed-up. The same is valid for the demographic attributes: if the frames are captured in the backyard of a kindergarten, one would that most of the subjects to be children. Ideally, a HAR dataset should provide images captured from multiple and variate scenes. Some datasets explicitly annotated the context in which the data was captured [75], while others address this issue by merging images from various datasets [152]. From another point of view, this leads our discussion to how the images from the datasets are presented. Generally speaking, the dataset provides the images either aligned (all the images have the same size and cropped around the human silhouette with a predefined margin; for example, [68]), or make the full video frame/image available and specify the bounding box of each human in the image. We consider that the latter approach is preferable, as it also incorporates context information and allows researches to decide how to handle the input data. 4. Binary attributes. Another question in database annotation is what happens when the attribute to annotate is indistinguishable due to low resolution and degraded images, occlusions, or other ambiguities. The majority of datasets tend to ignore this problem and classify the presence of an attribute or provide a multi-class attribute scheme. However, in a real-world setup, we cannot afford this luxury, as the case of indistinguishable attributes might occur quite frequently. Therefore, some datasets [161; 166] formulate the attribute classification task with N + 1 classes (+1 for the N/A label). This approach is preferable, as it allows taking both views over the data: depending on the application context, one could simply ignore the N/A attributes or, make the classification problem more interesting, integrate the N/A value into the classification framework. 5. **Camera configuration. ** Another aspect that should be taken into account when discussing HAR datasets is the camera setup used to capture the images or video sequences. We can distinguish between fixed-camera and moving-camera setups; obviously, this choice again depends on the application domain into which the HAR system will be integrated. For automotive applications or robotics, one should opt for a moving camera, as the camera movement might influence the visual properties of the human silhouettes. An example of a moving-camera dataset is Parse27k dataset [161]. For surveillance applications, a static camera setup will suffice. In another way, we could distinguish between indoor or outdoor camera setups; for example, RAP dataset [153] uses an indoor camera, while Parse27k dataset [161] comprises outdoor video sequences. Indoor captured datasets, such as [153], although captured in real-world scenarios, do not pose that many challenges as outdoor captured datasets, where the weather and lighting conditions are more volatile. Finally, the last aspect regarding the camera setup is related to the presence of a photographer. If the images are captured by a (professional) photographer some bias is introduced, as a human decides how and when to capture the images, such that it will enhance the appearance of the subject. Some databases, such as CAD [123] or HAT [66, 78] use images downloaded from public websites.