Abstract book (pdf) - ICPR 2010

spectral palm print data (420nm~1100nm). Our experiments showed that 2 spectral bands at 700nm and 960nm could provide
most discriminate information of palm print. This finding could be used as the guidance for designing multispectral palm
print systems in the future.
09:00-11:10, Paper TuAT9.5
Automatic Gender Recognition using Fusion of Facial Strips
Lee, Ping-Han, National Taiwan Univ.
Hung, Jui-Yu, National Taiwan Univ.
Hung, Yi-Ping, National Taiwan Univ.
We propose a fully automatic system that detects and normalizes faces in images and recognizes their genders. To boost the
recognition accuracy, we correct the in-plane and out-of-plane rotations of faces, and align faces based on estimated eye positions.
To perform gender recognition, a face is first decomposed into several horizontal and vertical strips. Then, a regression
function for each strip gives an estimation of the likelihood the strip sample belongs to a specific gender. The likelihoods
from all strips are concatenated to form a new feature, based on which a gender classifier gives the final decision. The proposed
approach achieved an accuracy of 88.1% in recognizing genders of faces in images collected from the World-Wide
Web. For faces in the FERET dataset, our system achieved an accuracy of 98.8%, outperforming all the six state-of-the-art
algorithms compared in this paper
09:00-11:10, Paper TuAT9.6
Benchmarking Local Orientation Extraction in Fingerprint Recognition
Cappelli, Raffaele, Univ. of Bologna
Maltoni, Davide, Univ. of Bologna
Turroni, Francesco, Univ. of Bologna
The computation of local orientations is a fundamental step in fingerprint recognition. Although a large number of approaches
have been proposed in the literature, no systematic quantitative evaluations have been done yet, mainly due to the lack of
proper datasets with associated ground truth information. In this paper we propose a new benchmark (which includes two
datasets and an accuracy metric) and report preliminary results obtained by testing four well-known local orientation extraction
algorithms.
09:00-11:10, Paper TuAT9.7
Efficient Finger Vein Localization and Recognition
Li, Xu, Civil Aviation Univ. of China
Yang, Jinfeng, Civil Aviation Univ. of China
In order to achieve accurate recognition of human finger vein (FV), this paper addresses the problems of finger vein localization
and vein feature extraction. An inherent physical property of human fingers is used to localize the region of interest
(ROI) of vein images as well as removing uninformative vein imagery based on the inter-phalangeal joint prior. In addition,
vein images are characterized as a series of energy features through steerable filters. Experimental results show the promising
performance of the proposed algorithm for human vein identification.
09:00-11:10, Paper TuAT9.8
Learning the Relationship between High and Low Resolution Images in Kernel Space for Face Super Resolution
Zou, Wilman, W W, Hong Kong Baptist Univ.
Yuen, Pong C, Hong Kong Baptist Univ.
This paper proposes a new nonlinear face super resolution algorithm to address an important issue in face recognition from
surveillance video namely, recognition of low resolution face image with nonlinear variations. The proposed method learns
the nonlinear relationship between low resolution face image and high resolution face image in (nonlinear) kernel feature
space. Moreover, the discriminative term can be easily included in the proposed framework. Experimental results on CMU-
PIE and FRGC v2.0 databases show that proposed method outperforms existing methods as well as the recognition based on
high resolution images.
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