Abstract
The identification of a person from gait images is generally sensitive to appearance changes, such as variations of clothes and belongings. One possibility to deal with this problem is to collect possible subjects' appearance changes in a database. However, it is almost impossible to predict all appearance changes in advance. In this paper, we propose a novel method, which allows robustly identifying people in spite of changes in appearance, without using a database of predicted appearance changes. In the proposed method, firstly, the human body image is divided into multiple areas, and features for each area are extracted. Next, a matching weight for each area is estimated based on the similarity between the extracted features and those in the database for standard clothes. Finally, the subject is identified by weighted integration of similarities in all areas. Experiments using the gait database CASIA show the best correct classification rate compared with conventional methods experiments.
Highlights
Person recognition systems have been used for a wide variety of applications, such as surveillance applications for wide area security operations and service robots that coexist with humans and provide various services in daily life
GEI improvements have been made and methods based on GEI have been proposed, such as gait flow image (GFI) [14], enhanced gait energy image (EGEI) [15], frame difference energy image (FDEI) [16], and dynamic gait energy image (DGEI) [17]
The main steps of the identification process are as follows: Step 1 An average image over a gait cycle is calculated, and the human body area is divided into multiple areas
Summary
Person recognition systems have been used for a wide variety of applications, such as surveillance applications for wide area security operations and service robots that coexist with humans and provide various services in daily life. Since image-based gait recognition is sensitive to appearance changes, such as variations of clothes and belongings, the correct classification rate is reduced in case the subject appearance is different from that in the database. Collins et al proposed a shape variation-based frieze pattern representation, which captures motion information by subtracting a silhouette image at a key frame from silhouettes at other times [27] In these three methods, the correct classification rate is reduced if the subject covers his shape with a big cloth, such as a long coat, due to the following reasons: (i) the dynamic area becomes small, so the discrimination capability of extracted features gets low; (ii) these methods utilize only dynamic features, but not static features that have strong discrimination capability.
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