Abstract book (pdf) - ICPR 2010

information in different scales of face images, we integrate the local decisions from various image resolutions. The proposed
multi-resolution block based face verification system is evaluated on the experiment 4 of Face Recognition Grand Challenge
(FRGC) version 2.0. We obtained 92.5% verification rate@0.1% FAR, which is the highest performance reported
on this experiment so far in the literature.
16:00-16:20, Paper TuCT1.2
Partial Face Biometry using Shape Decomposition on 2D Conformal Maps of Faces
Szeptycki, Przemyslaw, Ec. Centrale de Lyon
Ardabilian, Mohsen, Ec. Centrale de Lyon
Chen, Liming, Ec. Centrale de Lyon
Zeng, Wei, Wayne State Univ.
Gu, Xianfeng, State Univ. of New York at Stony Brook
Samaras, Dimitris, Stony Brook Univ.
In this paper, we introduce a new approach for partial 3D face recognition, which makes use of shape decomposition over
the rigid1 part of a face. To explore the descriptiveness of shape dissimilarity over an isometric part of a face, which has
lower probability to be influenced by expression, we transform a 3D shape to a 2D domain using conformal mapping and
use shape decomposition as a similarity measurement. In our work we investigate several classifiers as well as several
shape descriptors for recognition purposes. Recognition tests on a subset of the FRGC data set show approximately 80%
rank-one recognition rate using only the eyes and nose part of the face.
16:20-16:40, Paper TuCT1.3
Gender Classification using Interlaced Derivative Patterns
Shobeirinejad, Ameneh, Griffith Univ.
Gao, Yongsheng, Griffith Univ.
Automated gender recognition has become an interesting and challenging research problem in recent years with its potential
applications in security industry and human-computer interaction systems. In this paper we present a novel feature representation,
namely Interlaced Derivative Patterns (IDP), which is a derivative-based technique to extract discriminative
facial features for gender classification. The proposed technique operates on a neighborhood around a pixel and concatenates
the extracted regional feature distributions to form a feature vector. The experimental results demonstrate the effectiveness
of the IDP method for gender classification, showing that the proposed approach achieves 29.6% relative error
reduction compared to Local Binary Patterns (LBP), while it performs over four times faster than Local Derivative Patterns
(LDP).
16:40-17:00, Paper TuCT1.4
Heterogeneous Face Recognition: Matching NIR to Visible Light Images
Klare, Brendan, Michigan State Univ.
Jain, Anil, Michigan State Univ.
Matching near-infrared (NIR) face images to visible light (VIS) face images offers a robust approach to face recognition
with unconstrained illumination. In this paper we propose a novel method of heterogeneous face recognition that uses a
common feature-based representation for both NIR images as well as VIS images. Linear discriminant analysis is performed
on a collection of random subspaces to learn discriminative projections. NIR and VIS images are matched (I) directly
using the random subspace projections, and (ii) using sparse representation classification. Experimental results demonstrate
the effectiveness of the proposed approach for matching NIR and VIS face images.
17:00-17:20, Paper TuCT1.5
Clustering Face Carvings: Application to Devatas of Angkor Wat
Klare, Brendan, Michigan State Univ.
Mallapragada, Pavan Kumar, Michigan State Univ.
Jain, Anil, Michigan State Univ.
Davis, Kent, DatAsia Inc.
We propose a framework for clustering and visualization of images of face carvings at archaeological sites. The pairwise
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