Collarette Region Recognition based on Wavelets and Direct Linear Discriminant Analysis
Akanksha Joshi, Abhishek Kumar Gangwar, Zia Saquib · International Journal of Computer Applications · 2012
Iris recognition is seen as a highly reliable biometric technology.The performance of iris recognition is severely impacted when encountering poor quality images.The selection of the features subset and the classification is an important issue for iris biometrics.Here, we explored the contribution of collarette region in identifying a person.We applied five level haar wavelet decomposition for collarette region feature extraction and used the second level approximation coefficients combined with fifth level vertical coefficients for better accuracy.Then, we applied Direct Linear Discriminant Analysis (DLDA) to produce discriminative low-dimensional feature vectors.The approach is evaluated on CASIA Iris Interval database and we achieved 98.96% accuracy using the collarette region which is a significant improvement in the performance of recognition.