# Human Attribute Recognition--
## 6 Discussion
### Critical Discussion and Performance Comparison  
As mentioned, the main objective of the localization method is to extract distinct fine-grained features, by careful analyses of different pieces of the input data and aggregating them. Although the extracted localized features create a detailed feature representation of the image, dividing the image to several pieces has several drawbacks:  
* the expressiveness of the data is lost (e.g., when processing a jacket only by several parts, some global features that encode the jacket's shape and structure are ignored). * as the person detector cannot always provide aligned and accurate bounding boxes, rigid partitioning methods are prone to error in body-part captioning, mainly when the input data includes a wide background. Therefore, methods based on stride/grid patching of the image are not robust to misalignment errors in the person bounding boxes, leading to degradation in prediction performance. * different from gender and age, most human attributes (such as glasses, hat, scarf, shoes, etc.) belong to small regions of the image; therefore, analyzing other parts of the image may add irrelevant features to the final feature representation of the image. * some attributes are view-dependent and highly changeable due to human body-pose, and ignoring them reduces the model performance; for example, glasses recognition in the side-view images is more laborious than front-view, while it may be impossible in back-view images. Therefore, in some localization methods (e.g., pose-let based techniques), regardless of this fact, features of different parts may be aggregated to perform a prediction on an unavailable attribute. Figure 4: Frequency distribution of the labels describing the ‘Age’ class in the PETA [152] (on the **left**) and CRP [162] (on the **right**) databases. * some localization methods rely on the body-parsing techniques [176] or body-part detection methods [177] to extract local features. Not only requires training such part detectors rich annotations of data but also errors in body-parsing and body-part detection methods directly affect the performance of the HAR model. There are several possibilities to address some of these issues, which mostly attempt to guide the learning process using additional information. For instance, as discussed in Section 3, some works use novel model structures [72] to capture the relationships and correlations between the parts of the image, while others try to use prior body-pose coordinates [63] (or develop a view-detector in the main structure [61]) to learn the view-specific attributes. Some methods develop attention modules to find the relevant body parts, while some approaches extract various pose-lets [163] of the image by slicing-window detectors. Using the semantic attributes as a constraint for extracting the relevant regions is another solution to look for localized attributes [100]. Moreover, developing accurate body-part detectors, body-parsing algorithms and introducing datasets with part annotations are some strategies that can help the localization methods. Limited data is the other main challenge in HAR. The primary solutions for solving the problem of limited data are synthesizing artificial samples or augmenting the original data. One of the popular approaches for increasing the size of the dataset is to use generative models (i.e., Generative Adversarial Network (GAN) [178], Variational Auto-Encoders (VAE) [179], or a combination of both [180]). These models are powerful tools for producing new samples, but are not widely used for extending human full-body datasets for three reasons:  
* in opposition to the breakthrough in face generative models [181], full-body generative models are still in early stages and their performance is still unsatisfactory,
* the generated data is unlabelled, while HAR is yet far from the stage to be implemented based on unlabeled data. It worth mentioning that, automatic annotations is an active research area in object detection [182].