Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT9.54
Face Recognition across Pose with Automatic Estimation of Pose Parameters through AAM-Based Landmarking
Teijeiro-Mosquera, Lucía, Univ. de Vigo
Alba Castro, Jose Luis, Univ. of Vigo
Gonzalez-Jimenez, Daniel, Univ. of Vigo
In this paper we present a fully automatic system for face recognition across pose where no frontal view is needed in enrollment
or test. The system uses three Active Appearance Models(AAMs): the first one is a generic multi resolution AAM,
while the remaining ones are trained to cope with left/right variations (i.e. pose-dependent AAMs). During the fitting
stage, pose is automatically estimated using eigenvector analysis, and a synthetic face is generated through texture warping.
Results over CMU PIE Database show promising results compared to the performance achieved with manually land
marked faces.
09:00-11:10, Paper TuAT9.55
Cross-Spectral Face Verification in the Short Wave Infrared (SWIR) Band
Bourlai, Thirimachos, WVU
Kalka, Nathan, WVU
Ross, Arun, West Virginia Univ.
Cukic, Bojan, WVU
Hornak, Lawrence, WVU
The problem of face verification across the short wave infrared spectrum (SWIR) is studied in order to illustrate the advantages
and limitations of SWIR face verification. The contributions of this work are two-fold. First, a database of 50
subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is
performed in order to demonstrate the possibility of SWIR cross-spectral matching. Experiments also show that images
captured under different SWIR wavelengths can be matched to visible images with promising results. The role of multispectral
fusion in improving recognition performance in SWIR images is finally illustrated. To the best of our knowledge,
this is the first time cross-spectral SWIR face recognition is being investigated in the open literature.
09:00-11:10, Paper TuAT9.56
Decision Fusion for Patch-Based Face Recognition
Topçu, Berkay, Sabancı Univ.
Erdogan, Hakan, Sabanci Univ.
Patch-based face recognition is a recent method which uses the idea of analyzing face images locally, in order to reduce
the effects of illumination changes and partial occlusions. Feature fusion and decision fusion are two distinct ways to
make use of the extracted local features. Apart from the well-known decision fusion methods, a novel approach for calculating
weights for the weighted sum rule is proposed in this paper. Improvements in recognition accuracies are shown and
superiority of decision fusion over feature fusion is advocated. In the challenging AR database, we obtain significantly
better results using decision fusion as compared to conventional methods and feature fusion methods by using validation
accuracy weighting scheme and nearest-neighbor discriminant analysis dimension reduction method.
09:00-11:10, Paper TuAT9.57
Video based Palmprint Recognition
Methani, Chhaya, IIIT-H
Namboodiri, Anoop, International Inst. of Information Tech.
The use of camera as a biometric sensor is desirable due to its ubiquity and low cost, especially for mobile devices. Palm
print is an effective modality in such cases due to its discrimination power, ease of presentation and the scale and size of
texture for capture by commodity cameras. However, the unconstrained nature of pose and lighting introduces several
challenges in the recognition process. Even minor changes in pose of the palm can induce significant changes in the visibility
of the lines. We turn this property to our advantage by capturing a short video, where the natural palm motion
induces minor pose variations, providing additional texture information. We propose a method to register multiple frames
of the video without requiring correspondence, while being efficient. Experimental results on a set of different 100 palms
show that the use of multiple frames reduces the error rate from 12.75% to 4.7%. We also propose a method for detection
of poor quality samples due to specularities and motion blur, which further reduces the EER to 1.8%.
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