Forensic Writer Verification on Chinese Characters by Image Processing Techniques
Date Issued
2015
Date
2015
Author(s)
Lee, Pin-Xuan
Abstract
Handwriting is an informative kind of biometrics and writer verification plays a very important role in forensics. However, writer verification remains a challenging topic due to the large variations caused by the behavioral trait of individuals. In this thesis, two systems of writer verification are proposed to improve verification accuracy. The first method is to perform writer verification with local features and the support vector machine. These local features are generated by different combinations of detectors and descriptors, including the difference of Gaussian, the Harris corner, the SIFT descriptor and the oriented intensity patch. Since the amount of keypoints are various, a construction of codebook is required. K-means clustering is applied to build the codebook. Then, by the bag-of-word model, each handwriting image can be represented by a histogram with the codewords being indices. The histograms are the input feature vectors used for the support vector machine, which is a famous technique in machine learning. Later, global features are also explored in this research. So another method based on global features is proposed. This verification performs even better by using log-Gabor features, some advanced moments and features extracted from gray level co-occurrence matrices. By combining these features, the system shows superior robustness for traditional verification problems. Besides, a more flexible classification framework is proposed. Though the support vector machine leads to accurate results, it is confined to the amount of training data. To prevent overfitting, another classification method based on the weighted squared Euclidean distance is devised for the case of insufficient or unbalanced training data. From the results of simulation, the accuracies can reach about 92.7% and 83.5% for the proposed framework with/without the support vector machine, respectively, which outperform other popular identification or verification methods, including the local binary pattern, the local directional pattern, Gabor features, the stroke fragment histogram and the curve fragment code.
Subjects
writer verification
difference of Gaussian
Harris corner
scale-invariant feature transform
oriented intensity patch
K-means clustering
support vector machine
log-Gabor feature
moment invariant
gray level co-occurrence matrix
weighted squared Euclidean distance
Type
thesis