# Human Attribute Recognition--
## 1 Introduction
### Motivation And Applications  
Human attribute recognition methods extract semantic features that describe human-understandable characteristics of the individuals in a scene, either from images or video sequences, ranging from demographic information (gender, age, race/ethnicity), appearance attributes (body weight, face shape, hairstyle and color etc. ), emotional state, to the motivation and attention of people (head pose, gaze direction). As they provide vital information about humans,such systems have already been integrated into numerous real-world applications, and are entwined with many technologies across the globe. Indisputably, HAR is one of the most important steps in any visual surveillance system. Biometric identifiers are extracted to identify and distinguish between the individuals. Based on the biometric traits, humans are uniquely identified, either based on their facial appearance [17; 18; 19], iris patterns [20] or on behavioral traits (gait) [21; 22]. With the increase of surveillance cameras worldwide, the research focus has shifted from (hard-)biometric (iris recognition, palm-print) to soft biometric identifiers. The latter describe human characteristics, taxonomized into a humanly understandable manner, but are not sufficient to uniquely differentiate between individuals. Instead, they are descriptors used by humans to categorize their peers into several classes. On a top level, HAR applications can be divided into three main categories: _security and safety_, _research_ directions, and _commercial applications_. Yielding high-level semantic information, HAR could provide auxiliary information for different computer vision tasks, such as person re-identification ([23; 24]), human action recognition [25], scene understanding, advanced driving assistance systems, and event detection ([26]). Another fertile field where HAR could be applied is in human drone surveillance. Drones or Unmanned Aerial Vehicles (UAV), although initially designed for military applications, are rapidly extending to various other application domains, due to their reduced size, swiftness, and ability to navigate through remote and dangerous environments. Researchers in multiple fields have started to use UAVs drones in their research work, and, as a result, the Scopus database has shown an increase in the papers related to UAVs, from 11 (4.7 \(\times\) 10\({}^{6}\) of total papers) papers published in 2009 to 851 (270.0 \(\times\) 10\({}^{6}\) of total articles) published in 2018 [27]. In terms of human surveillance, drones have been successfully used in various scenarios, ranging from rescue operations and victim identification, people counting and crowd detection, to police activities. All these applications require information about human attributes. Nowadays, researchers in universities and major car industries work together to design and build the self-driving cars of the future. HAR methods have important implications in such systems as well. Although numerous papers addressed the problem of pedestrian detection, pedestrian attribute recognition is one of the keys to future improvements. Cues about the pedestrians' body and head orientation provide insights about their intent, and thus avoiding collisions. The pedestrians' age is another aspect that should be analyzed by advanced driving assistance systems to decrease vehicle speed when children are on the sidewalk. Finally, other works suggest that even pedestrians' accessories could be used to avoid collisions: starting from the statistical evidence that collisions between pedestrians and vehicles are more frequent on rainy days, in Reference [28] authors suggest that detecting whether a pedestrian has on open umbrella could reduce traffic incidences. As mentioned above, the applications of biometric cues are not limited to surveillance systems. Such traits have necessary implications also in commercial applications (logins, medical records management) and government applications (ID cards, border, and passport control) [29].