Keywords : Recognition


Adoption of the Co-Occurrence Matrix and Artificial Neural Networks in Fingerprint Recognition

Maysoon Khidr Al-Nuaimi

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 1, Pages 127-136
DOI: 10.33899/csmj.2013.163446

This paper presents a new method for fingerprint recognition depending on various sizes of fingerprint images. The proposed algorithm applied on more than 30 fingerprint samples, the results was good.
The proposed algorithm begins with apply enhancement operations on the fingerprint image to eliminate unwanted noise around the fingerprint by using median filter. Then apply thinning operation on the enhanced image and compute co-occurrence matrices for produced image. Next, the properties of the co-occurrence matrices used as inputs of the neural network for recognition process. To speed the recognition process back propagation network used. The ratio of recognition  about 100%.
 

Recognition of Eudiscoaster and Heliodiscoaster using SOM Neural Network

Raid R. AL-Nima

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 141-152
DOI: 10.33899/csmj.2010.163918

This research is aimed to design an Eudiscoaster and Heliodiscoaster recognition system. There are two main steps to verify the goal. First: applying image processing techniques on the fossils picture for data acquisition. Second: applying neural networks techniques for recognition.
The image processing techniques display the steps for getting a very clear image necessary for extracting data from the acquisition of image type (.jpg). This picture contains the fossils. The picture should be enhanced to bring out the pattern. The enhanced picture is segmented into            144 parts, then an average for every part can easily be computed. These values will be used in the neural network for the recognition.
For neural network techniques, Self Organization Maps (SOM) neural network was used for clustering. The weights and output values will be stored to be used later in identification. The SOM network succeeded in identification and attained to (False Acceptance Rate = 15% - False Rejection Rate = 15%).
 
 

Iris Recognition System Based on Wavelet Transform

Maha A. Hasso; Bayez K. Al-Sulaifanie; Kaydar M. Quboa

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 2, Pages 105-116
DOI: 10.33899/csmj.2009.163801

In order to provide accurate recognition of individuals, the most discriminating information present in an iris pattern must be extracted. Only the significant features of the iris must be encoded so that comparisons between templates can be made. Most iris recognition systems make use of a band pass decomposition of the iris image to create a biometric template. In this paper, the feature extraction techniques are improved and implemented. These techniques are using wavelet filters. The encoded data by wavelet filters are converted to binary code to represent the biometric template. The Hamming distance is used to classify the iris templates, and the False Accept Rate (FAR), False Reject Rate (FRR)  and recognition rate (RR)  are calculated [1]. 
            The wavelet transform using DAUB12 filter proves that it is a good feature extraction technique. It gives equal FAR and FRR  and a high recognition rate for the two used databases. When applying the DAUB12 filter to CASIA database, the FAR and FRR are equal to 1.053%, while the recognition rate is 97.89%. For Bath database the recognition rate when applying DAUB12 filter is 100%. CASIA and Bath databases are obtained through personal communication. These databases are used in this paper.
 

Personal Identification with Iris Patterns

Mazin R. Khalil; Mahmood S. Majeed; Raid R. Omar

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 1, Pages 13-26
DOI: 10.33899/csmj.2009.163762

This research is aimed to design an iris recognition system. There are two main steps to verify the goal. First: applying image processing techniques on the picture of an eye for data acquisition. Second: applying neural networks techniques for identification.
The image processing techniques display the steps for getting a very clear iris image necessary for extracting data from the acquisition of eye image. This picture contains all the eye (iris, pupil and lashes). So, the localization of the iris is very important. The new picture should be enhanced to bring out the pattern. The enhanced picture is segmented into 100 parts, then a standard Deviation (STD) can easily be computed for every part. These values will be used in the neural network for the identification.
For neural network techniques, Backprobagation neural network was used for comparisons. The weights and output values will be stored in a text file to be used later in identification. The Backprobagation network succeeded in identification and attained to (False Acceptance Rate = 10% - False Rejection Rate = 10%).