AIRCC's International Journal of Computer Science and Information Technology

B. S. El-Desouky ¹, M. El-Kady ², M. Z. Rashad ³, Mahmoud M. Eid ¹

Affiliations
1 Mathematics Department, Mansoura University, EG
2 Mathematics Department, Helwan University, EG
3 Computer Science Department, Mansoura University, EG

Abstract

Personal identification using 2D ear images still has many problems such as occlusion mostly caused by hair, earrings, and clothes. To avoid this problem, we propose to divide the ear image into non-overlapping equal divisions and identify persons through these non-occluded parts separately and then combine outputs of the classification of these parts in abstract, rank, and measurement level fusion. Experimental results show that the increasing of recognition rate through combining small parts of non-occluded divisions of ear image.

Keywords

Ear Recognition, LDA (Linear Discriminante Analysis), DCT (Discrete Cosine Transform), Combining Classifiers.

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M. Z. Rashad ¹, B. S. el-Desouky ², Manal S. Khawasik ¹

Affiliations
1 Dept. of Computer Science, Mansoura University, EG
2 Dept. of the Mathematics, Mansoura University, EG

Abstract

This paper introduces an approach of plant classification which is based on the characterization of texture properties. We used the combined classifier learning vector quantization. We randomly took out 30 blocks of each texture as a training set and another 30 blocks as a testing set. We found that the combined classifier method gave a high performance which is a superior than other tested methods. The experimental results indicated that our algorithm is applicable and its average correct recognition rate was 98.7%.

Keywords

Plant Classification, Learning Vector Quantization(LVQ), Radial Base Function(RBF), Texture Classification, Neural Networks, Texture Classification.

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