Abstract book (pdf) - ICPR 2010

WeAT5 Topkapı Hall B
Face Analysis Regular Session
Session chair: Lovell, Brian Carrington (The Univ. of Queensland)
09:00-09:20, Paper WeAT5.1
Face Sketch Synthesis via Sparse Representation
Chang, Liang, Beijing Normal Univ.
Zhou, Mingquan, Beijing Normal Univ.
Han, Yanjun, Chinese Acad. of Sciences
Deng, Xiaoming, Chinese Acad. of Sciences
Face sketch synthesis with a photo is challenging due to that the psychological mechanism of sketch generation is difficult
to be expressed precisely by rules. Current learning-based sketch synthesis methods concentrate on learning the rules by
optimizing cost functions with low-level image features. In this paper, a new face sketch synthesis method is presented,
which is inspired by recent advances in sparse signal representation and neuroscience that human brain probably perceives
images using high-level features which are sparse. Sparse representations are desired in sketch synthesis due to that sparseness
can adaptively selects the most relevant samples which give best representations of the input photo. We assume that
the face photo patch and its corresponding sketch patch follow the same sparse representation. In the feature extraction,
we select succinct high-level features by using the sparse coding technique, and in the sketch synthesis process each sketch
patch is synthesized with respect to high-level features by solving an $l_1$-norm optimization. Experiments have been
given on CUHK database to show that our method can resemble the true sketch fairly well.
09:20-09:40, Paper WeAT5.2
Restoration of a Frontal Illuminated Face Image based on KPCA
Xie, Xiaohua, Sun Yat-sen Univ.
Zheng, Wei-Shi, Queen Mary Univ. of London
Lai, Jian-Huang, Sun Yat-sen Univ.
Suen, Ching Y.
In this paper, we propose a novel illumination-normalization method. By using the combination of the Kernel Principal
Component Analysis (KPCA) and Pre-image technology, this method can restore the frontal-illuminated face image from
a single non-frontal-illuminated face image. In this method, a frontal-illumination subspace is first learned by KPCA. For
each input face image, we project its large-scale features, which are affected by illumination variations, onto this subspace
to normalize the illumination. Then the frontal-illuminated face image is reconstructed by combining the small- and the
normalized large- scale features. Unlike most existing techniques, the proposed method does not require any shape modeling
or lighting estimation. As a holistic reconstruction, KPCA+Pre-image technology incurs less local distortion. Compared
to directly applying KPCA+Pre-image technology on the original image, our proposed method can be better at
processing an image of a face that is outside the training set. Experiments on CMU-PIE and Extended Yale B face databases
show that the proposed method outperforms state-of-the-art algorithms.
09:40-10:00, Paper WeAT5.3
A Bayesian Approach to Face Hallucination using DLPP and KRR
Tanveer, Muhammad, National Univ. of Science and Tech.
Rao, Naveed Iqbal, National Univ. of Sciences and Tech.
Low resolution faces are the main barrier to efficient face recognition and identification in several problems primarily
surveillance systems. To mitigate this problem we proposes a novel learning based two-step approach by the use of Direct
Locality Preserving Projections (DLPP), Maximum a posterior estimation (MAP) and Kernel Ridge Regression (KRR)
for super-resolution of face images or in other words Face Hallucination. First using DLPP for manifold learning and
MAP estimation, a smooth Global high resolution image is obtained. In second step to introduce high frequency components
KRR is used to model the Residue high resolution image, which is then added to Global image to get final high
quality detail featured Hallucinated face image. As shown in experimental results the proposed system is robust and
efficient in synthesizing low resolution faces similar to the original high resolution faces.
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