Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT9.42
Learning Sparse Face Features : Application to Face Verification
Buyssens, Pierre, Greyc UMR6072
Revenu, Marinette, GREYC UMR 6072
We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network
is trained to reconstruct a reference per subject image. In classical feature–based approaches, a first stage of features extraction
is followed by a classification to perform the recognition. In classical Convolutional Neural Network approaches,
features extraction stages are stacked (interlaced with pooling layers) with classical neural layers on top to form the complete
architecture of the network. This paper addresses two questions : 1. Does a pretraining of the filters in an unsupervised
manner improve the recognition rate compared to the one with filters learned in a purely supervised scheme ? 2. Is there
an advantage of pretraining more than one feature extraction stage ? We show particularly that a refinement of the filters
during the supervised training improves the results.
15:00-17:10, Paper MoBT9.43
Image Feature Extraction using 2D Mel-Cepstrum
Cakir, Serdar, Bilkent Univ.
Cetin, E., Bilkent Univ.
In this paper, a feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. Feature matrices
resulting from the 2D mel-cepstrum, Fourier LDA approach and original image matrices are individually applied to the
Common Matrix Approach (CMA) based face recognition system. For each of these feature extraction methods, recognition
rates are obtained in the AR face database, ORL database and Yale database. Experimental results indicate that recognition
rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA approach
and raw image matrices. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems.
15:00-17:10, Paper MoBT9.44
Entropy Estimation and Multi-Dimensional Scale Saliency
Suau, Pablo, Univ. of Alicante
Escolano, Francisco, Univ. of Alicante
In this paper we survey two multi-dimensional Scale Saliency approaches based on graphs and the k-d partition algorithm.
In the latter case we introduce a new divergence metric and we show experimentally its suitability. We also show an application
of multi-dimensional Scale Saliency to texture discrimination. We demonstrate that the use of multi-dimensional
data can improve the performance of texture retrieval based on feature extraction.
15:00-17:10, Paper MoBT9.45
A Novel Facial Localization for Three-Dimensional Face using Multi-Level Partition of Unity Implicits
Hu, Yuan, Shanghai Jiao Tong Univ.
Yan, Jingqi, Shanghai Jiao Tong Univ.
Li, Wei, Shanghai Jiao Tong Univ.
Shi, Pengfei, Shanghai Jiao Tong Univ.
This paper presents a novel facial localization method for 3D face in the presence of facial pose and expression variation.
An idea of using Multi-level Partition of Unity (MPU) Implicits in a hierarchical way is proposed for reconstruction of
face surface. Based on the analysis of curvature features, nose and eyeholes regions can be detected on lower level reconstructed
face surface uniquely. Experimental results show that this method is invariant to pose, holes, noise and expression.
The overall performance of 99.18% is achieved.
15:00-17:10, Paper MoBT9.46
Automated Feature Weighting in Fuzzy Declustering-Based Vector Quantization
Ng, Theam Foo, Univ. of New South Wales@ADFA
Pham, Tuan D., Univ. of New South Wales@ADFA
Sun, Changming, CSIRO
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