1. 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
Abstract
Recognition Techniques
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.
The high-level figure illustrates some human recognition approaches followed in recent researches:
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].
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’.
Classifier
Input Video
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.
b) Appearance based approach: binary silhouette is
extracted from input video sequence to form a
compact representation [4][5].
Human
Recognition
Result
Research Infrastructure
A gait research infrastructure for human identification or activity recognition
needs three basic components [1]
A Large Data Set
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
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
A set of experiments are designed to investigate the effect of various factors
(walking surface, shoe type, camera direction etc.) on recognition
performances.
Research Challenges
Standard Gait Data Sets
 Improved recognition performance and accuracy
 USF Human ID Data Set
 View and direction independent recognition
(http://www.gaitchallenge.org)
 Recognition in public space and crowd
 Southampton HiD database
 Consideration of illumination, strength and direction of light
(http://www.gait.ecs.soton.ac.uk)
 Recognition from obscured body part
 CMU Mobo data set
 Recognition with older dataset
 Others
 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
Conclusion
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.
Contacts: {Mahfuzul.Haque, Manoranjan.Paul, Manzur.Murshed} @infotech.monash.edu.au
[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