# Human Attribute Recognition--
## 1 Introduction  
Over recent years, the increasing amount of multimedia data available in the Internet or supplied by CCTV devices deployed in public/private environments has been raising the requirements for solutions able to automatically analyse human appearance, features and behavior. Hence, Human Attribute Recognition (HAR) has been attracting increasing attentions in the computer vision/pattern recognition domains, mainly due to its potential usability for a wide range of applications (e.g., crowd analysis [1], person search [2, 3], detection [4], tracking [5], and re-identification [6]). HAR aims at describing and understanding the subjects' traits (such as their hair color, clothing style [7], gender [8], etc.) either from full-body or facial data [9]. Generally, there are four main sub-categories in this area of study:  
* Facial Attribute Analysis (FAA). Facial attribute analysis aims at estimating the facial attributes or manipulating the desired attributes. The former is usually carried out by extracting a comprehensive feature representation of the face image, followed by a classifier to predict the face attributes. On the other hand, in manipulation works, face images are modified (e.g., glasses are removed or added) using generative models. * Full-body Attribute Recognition (FAR). Full-body attribute recognition regards the task of inferring the soft-biometric labels of the subject, including clothing style, head-region attributes, recurring actions (talking to the phone) and role (cleaning lady, policeman), regardless of the location or body position (eating in a restaurant). * Pedestrian Attribute Recognition (PAR). As an emerging research sub-field of HAR, it focuses on the full-body human data that have been exclusively collected from video surveillance cameras or panels, where persons are captured while walking, standing, or running. * Clothing Attribute Analysis (CAA). Another sub-field of human attribute analysis that is exclusively focused on clothing style and type. It comprises several sub-categories such as in-shop retrieval, costumer-to-shop retrieval, fashion landmark detection, fashion analysis, and cloth attribute recognition, each of which requires specific solutions to handle the challenges in the field. Among these sub-categories, cloth attribute recognition is similar to pedestrian and full-body attribute recognition and studies the clothing types (e.g., texture, category, shape, style). The typical pipeline of the HAR systems is given in Figure 1, which indicates the requirement of a dataset preparation prior to designing a model. As shown in Figure 1, preparing a dataset for this problem typically comprises four steps:  
1. Capturing raw data, which can be accomplished using mobile cameras (e.g., drone) or stationary cameras (e.g., CCTV). Also, the raw data might even be collected from images/videos publicly available (e.g., _Youtube_, or similar sources). 2. In most supervised training approaches, HAR models consider one person at a time (instead of analyzing a full-frame with multiple persons). Therefore, detecting the bounding boxes of each subject is essential and can be done by state-of-the-art object detection solutions (i.e., Mask R-CNN [10], You Only Look Once (YOLO) [11], Single Shot Detection (SSD) [12], etc.) 3. If the raw data is in video format, spatio-temporal information should be kept. in such cases, the accurate tracking of each object (subject) in the scene can significantly ease the annotation process. 4. Finally, in order to label the data with semantic attributes, all the bounding boxes of each individual are displaced to human annotators. based on human perception, the desired labels (e.g., 'gender' or 'age') are then associated to each instance of the dataset. Regarding the data-type and available annotations, there are many possibilities for designing HAR models. Early researches were based on crafted feature extractors.