Improved recognition performance and accuracy

View and direction independent recognition

Recognition in public space and crowd

Consideration of illumination, strength and direction of light

Recognition from obscured body part

Recognition with older dataset

Recognition from varying speed of the subjects

Activity recognition by analyzing group dynamics of people

Creating a generic gait recognition classifier for activity recognition and human identification

GAIT RECOGNITION FROM VIDEO-SEQUENCE FOR HUMAN IDENTIFICATION : CHALLENGES

Mahfuzul Haque, Manoranjan Paul and Manzur Murshed Gippsland School of Information Technology, Monash University, Victoria 3842, Australia

Gait is the particular way one walks and an emergent biometric feature for

identifying people and activities. There are a number of biometric characteristics

exist and are in use like: DNA, Face, Fingerprint, Retinal Scan etc. In recent years

gait is receiving more attention to the researchers as it can be captured from a great

distance using video cameras without any physical contact, as a result a sound gait

recognition system can be the basis of robust video surveillance system. There are

still many challenges to the researchers towards robust gait recognition systems

for real-world applications.

[1] The humanID gait challenge problem: data sets, performance, and analysis. Sarkar et al. IEEE Trans., 2005

[2] Fusion of static and dynamic body biometrics for gait recognition. Liang Wang et al. IEEE Trans., 2004

[3] A bayesian framework for extracting human gait using strong prior knowledge. Ziheng et al. IEEE Trans., 2006

[4] Feature fusion of face and gait for human recognition at a distance in video. Xiaoli et al. ICPR 2006.

[5] Matching shape sequences in video with applications in human movement analysis. Veeraraghavan et al. IEEE Trans., 2005

Contacts: {Mahfuzul.Haque, Manoranjan.Paul, Manzur.Murshed} @infotech.monash.edu.au

Recognition Techniques

System is trained with

training video sequences of

the gallery to be identified.

System performance is

tested with test

video sequences.

System parameters

are adjusted for improved

performance.

Abstract

Conclusion

Research Infrastructure

Standard Gait Data Sets Research Challenges

Gait Recognition System Construction

The set of people recognized by a recognition system is called ‘Gallery’ and a video sequence given to the system to be recognized is known as ‘Probe’.

A Large Data Set

A Recognition Algorithm

A Set of Experiments

A large gait video data set of the natural movements of people and group of

people in different illumination and direction settings.

A recognition algorithm takes gait video sequences as input and produces a

recognition result as output. Techniques followed in recent researches:

Manifold learning, Bayesian framework, Hidden Markov Model etc.

A set of experiments are designed to investigate the effect of various factors

(walking surface, shoe type, camera direction etc.) on recognition

performances.

A gait research infrastructure for human identification or activity recognition

needs three basic components [1]

USF Human ID Data Set

(http://www.gaitchallenge.org)

Southampton HiD database

(http://www.gait.ecs.soton.ac.uk)

CMU Mobo data set

Others

Though, a number of significant research works have been done for gait recognition, the accuracy is not

up to the mark for real applications. Thus, there is a great research potential in this area by addressing

the research challenges.

Input Video

Static feature extraction

Height, Head Radius, Build, Length of limbs

Dynamic feature extraction

a) Model based approach: input video sequence is

mapped to a 2D/3D model [2][3].

b) Appearance based approach: binary silhouette is

extracted from input video sequence to form a

compact representation [4][5].

Human

Recognition

Result

Classifier

The high-level figure illustrates some human recognition approaches followed in recent researches: