Abstract book (pdf) - ICPR 2010

Lei, Huang, Chinese Acad. of Sciences
Liu, Changping, Chinese Acad. of Sciences
Many previous image processing methods discard low-frequency components of images to extract illumination invariant
for face recognition. However, this method may cause distortion of processed images and perform poorly under normal
lighting. In this paper, a new method is proposed to deal with illumination problem in face recognition. Firstly, we define
a score to denote a relative difference of the first and second largest similarities between the query input and the individuals
in the gallery classes. Then, according to the score, we choose the appropriate images, raw or processed images, to involve
the recognition. The experiment in ORL, CMU-PIE and Extended Yale B face databases shows that our adaptive method
give more robust result after combination and perform better than the traditional fusion operators, the sum and the maximum
of similarities.
14:50-15:10, Paper WeBT5.5
Discriminative Prototype Learning in Open Set Face Recognition
Han, Zhongkai, Tsinghua Univ.
Fang, Chi, Tsinghua Univ.
Ding, Xiaoqing, Tsinghua Univ.
We address the problem of prototype design for open set face recognition (OSFR) using single sample image. Normalized
Correlation (NC), also known as Cosine Distance, offers many benefits in accuracy and robustness compared to other distance
measurement in OSFR problem. Inspired by classical Learning Vector Quantization (LVQ), a novel discriminative
learning method is proposed to design a discriminative prototype used by NC classifier. Specifically, we develop an objective
function that fixes the NC score between the prototype and within-class sample at a high level and minimizes the
similarity between the prototype and between-class samples. Several experiments conducted on benchmark databases
demonstrate the superior performance of the prototype designed compared to the original one.
WeBT6 Anadolu Auditorium
Document Analysis - II Regular Session
Session chair: Lopresti, Daniel (Lehigh Univ.)
13:30-13:50, Paper WeBT6.1
On-Line Handwriting Word Recognition using a Bi-Character Model
Prum, Sophea, Univ. of La Rochelle
Visani, Muriel, Univ. of La Rochelle
Ogier, Jean-Marc, Univ. de la Rochelle
This paper deals with on-line handwriting recognition. Analytic approaches have attracted an increasing interest during
the last ten years. These approaches rely on a preliminary segmentation stage, which remains one of the most difficult
problems and may affect strongly the quality of the global recognition process. In order to circumvent this problem, this
paper introduces a bi-character model, where each character is recognized jointly with its neighboring characters. This
model yields two main advantages. First, it reduces the number of confusions due to connections between characters
during the character recognition step. Second, it avoids some possible confusion at the character recognition level during
the word recognition stage. Our experimentation on significant databases shows some interesting improvements of the
recognition rate, since the recognition rate is increased from 65% to 83% by using this bi-character strategy.
13:50-14:10, Paper WeBT6.2
Ruling Line Removal in Handwritten Page Images
Lopresti, Daniel, Lehigh Univ.
Kavallieratou, Ergina, Univ. of the Aegean
In this paper we present a procedure for removing ruling lines from a handwritten document image that does not break existing
characters. We take advantage of common ruling line properties such as uniform width, predictable spacing, position
vs. text, etc. The proposed process has no effect on document images without ruling lines, hence no a priori discrimination
is required. The system is evaluated on synthetic page images in five different languages.
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