Recognizing Persons with One-Shot Learning

Abstract

There have been several attempts to solve the problem of Human Recognition i.e. the ability to identify individual persons in novel situations. Using facial features (e.g. Wiskott et al, Facial Recognition using Elastic Bunch Graph Matching, 1997) for this purpose has proved to be quite successful. However when a person is at a appreciable distance, then the facial resolution is insufficient for reliable recognition. Therefore, some systems use additional information such as: Walking Patterns (Collins et al, Silhouette-based Human Identification from Body Shape and Gait, 2002) or distinguish color and shape features using Support Vector Machine classifiers (Nakajima et al, Full-body Person Recognition System,2003). We present here a simple system, which is able to recognize and track people from video sequences in real time. The implemented system learns the representation of the person using just a single video sequence (one-shot), with enough detail to permit later recognition and enough generality to deal with variation. To achieve this we divide the image of a person into regions: head, torso and legs, using a minimal model of the human body (corresponding to a virtually naive spectator). It learns the color and texture features for each region and stores them in a database of people. Thereafter, for recognition, it computes a similarity function between the input 'instance' and each person in the database. The person that generates the maximum similarity is chosen as the recognized person (this similarity value often exceeds the other people in the database by over three orders of magnitude). Furthermore, since there is no specific parameter tuning required for either learning or recognition, the system illustrates superior ability for automatic visual surveillance. This system could be used in conjunction with a face recognition system to reduce the search space for faces, by narrowing down the number of possibilities based on person recognition