Statistical features from frame aggregation and differences for human gait recognition

Title

Statistical features from frame aggregation and differences for human gait recognition

Creator

K S.; Wahid F.F.; G R.

Description

Human gait recognition, an alternate biometric technique, received significant attention in the last decade. As many gait recognition applications require real-time response, the primary concern is to design efficient and straightforward gait features for human recognition. In this work, two novel gait features are proposed. Both features are designed by exploring the dynamic variations of different body parts during a gait cycle. The first feature set is based on one-against-all gait frame differences for person identification. This novel approach divides each frame in a gait cycle to blocks, compute the block sum, and then find the difference of respective block sum between the first frame and the rest. The second feature set is defined on the first-order statistics of the normalized sum of the frames in a cycle. Two other existing features- Centroid of Silhouette frames and feature values defined on Change Energy Images are also considered. Feature level fusion is realized by considering the different combinations of the four types of features. Experiments carried out with the CASIA Gait Dataset B demonstrated the proposals merit with high recognition accuracy. The outcome of the investigations is promising when compared to recent contributions. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Source

Multimedia Tools and Applications, Vol-80, No. 12, pp. 18345-18364.

Date

2021-01-01

Publisher

Springer

Subject

All block differences; Frame aggregation features; Human gait recognition; Inter-frame differences; One-against; Silhouette centroid

Coverage

K S., Department of Information Technology, Kannur University, Kannur, Kerala, India; Wahid F.F., Department of Information Technology, Kannur University, Kannur, Kerala, India; G R., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 13807501; CODEN: MTAPF

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s11042-021-10655-z

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101140841&doi=10.1007%2fs11042-021-10655-z&partnerID=40&md5=fe5bb6f38ed9ac4153e0fd0d390c1e7e