As the application of human-machine interface and the development of digitalization have advanced tremendously, person identification technology has been developed in a great extent and may be categorized into a short-distance aspect and a long-distance aspect.
The short-distance person identification may be further subcategorized into a human biometric feature basis and a human behaviour feature basis. The former technique (e.g. face, fingerprint, and iris recognition) has been well-developed and provides accurate results, and yet it may only be suitable for certain scenarios such as the user facing toward a camera lens or placing his/her finger on a fingerprint sensor. The latter technique (e.g. voiceprint or signature recognition) may also require the user to perform certain actions, and such technique may involve short-distance or contact requirements and thereby limit its application.
On the other hand, the long-distance person identification is a non-contact approach (e.g. gait and outfit recognition) that may provide a higher flexibility and may be suitable for surveillance or robot vision. However, human body features extracted by the long-distance approach may be insufficient and indefinite. In terms of gait recognition, despite its uniqueness and inherent difficulty of imitation, a sequence of images may be required for identification, and the entire process may be time-consuming. Moreover, the process of gait recognition may be affected by the user's injury or the comfortability of the user's outfit. In terms of outfit recognition, since there may exist a huge variation in a same user's outfits and since different users may have similar outfits, the uniqueness of such technique is not guaranteed. Hence, the existing solutions for long-distance person identification may not be effective.