Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper ThAT9.46
On-Line Signature Verification using 1-D Velocity-Based Directional Analysis
Muhammad Talal Ibrahim, Ryerson Unviersity
Matthew, Kyan, Ryerson Unviersity
M. Aurangzeb, Khan, COMSATS Inst. of Information Tech.
Ling, Guan, Ryerson Unviersity
In this paper, we propose a novel approach for identity verification based on the directional analysis of velocity-based
partitions of an on-line signature. First, inter-feature dependencies in a signature are exploited by decomposing the shape
(horizontal trajectory, vertical trajectory) into two partitions based on the velocity profile of the base-signature for each
signer, which offers the flexibility of analyzing both low and high-curvature portions of the trajectory independently. Further,
these velocity-based shape partitions are analyzed directionally on the basis of relative angles. Support Vector Machine
(SVM) is then used to find the decision boundary between the genuine and forgery class. Experimental results demonstrate
the superiority of our approach in on-line signature verification in comparison with other techniques.
09:00-11:10, Paper ThAT9.47
Age Classification based on Gait using HMM
Zhang, De, Beihang Univ.
Wang, Yunhong, Beihang Univ.
Bhanu, Bir, Univ. of California
In this paper we propose a new framework for age classification based on human gait using Hidden Markov Model (HMM).
A gait database including young people and elderly people is built. To extract appropriate gait features, we consider a contour
related method in terms of shape variations during human walking. Then the image feature is transformed to a lowerdimensional
space by using the Frame to Exemplar (FED) distance. A HMM is trained on the FED vector sequences.
Thus, the framework provides flexibility in the selection of gait feature representation. In addition, the framework is robust
for classification due to the statistical nature of HMM. The experimental results show that video-based automatic age classification
from human gait is feasible and reliable.
09:00-11:10, Paper ThAT9.48
Human Electrocardiogram for Biometrics using DTW and FLDA
N, Venkatesh, Tata Consultancy Services Innovation Lab.
Jayaraman, Srinivasan, Tata Consultancy Services, Bangalore
This paper proposes a new approach for person identification and novel person authentication using single lead human
Electrocardiogram. Nine Feature parameters were extracted from ECG in spatial domain for classification. For person
identification, Dynamic Time Warping (DTW) and Fisher‘s Linear Discriminant Analysis (FLDA) with K-Nearest Neighbor
Classifier (NNC) as single stage classification yielded a recognition accuracy of 96% and 97% respectively. To further
improve the performance of the system, two stage classification techniques have been adapted. In two stage classifications
FLDA is used with k-NNC at the first stage followed by DTW classifier at the second stage which yielded 100% recognition
accuracy. During person authentication we adapted the QRS complex based threshold technique. The overall performance
of the system was 96% for both legal and intruder situations is verified for MIT-BIH normal database size of 375 recording
from 15 individual ECG.
09:00-11:10, Paper ThAT9.49
Recognizing Sign Language from Brain Imaging
Mehta, Nishant, Georgia Inst. of Tech.
Starner, Thad, Georgia Inst. of Tech.
Moore Jackson, Melody, Georgia Inst. of Tech.
Babalola, Karolyn, Georgia Inst. of Tech.
James, George Andrew, Univ. of Arkansas
Classification of complex motor activities from brain imaging is relatively new in the fields of neuroscience and braincomputer
interfaces (BCIs). We report sign language classification results for a set of three contrasting pairs of signs. Executed
sign accuracy was 93.3%, and imagined sign accuracy was 76.7%. For a full multiclass problem, we used a decision
directed acyclic graph of pairwise support vector machines, resulting in 63.3% accuracy for executed sign and 31.4% ac-
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