In conventional iris recognition methods, due to the difficulty of selecting one optimal wavelet filter for iris feature extraction, multiple wavelet filters (with different frequencies and kernel sizes) are adopted. However, this causes the processing time and the extracted feature size to increase. To overcome this problem, feature level fusion of the extracted iris features has been proposed, but this method requires a complicated dimension reduction procedure. Therefore, we propose a new iris recognition method based on score level fusion, using two Gabor wavelet filters and SVM (support vector machine). For score level fusion, we used the typical HD (Hamming distance) produced by a Gabor filter, which can easily be applied to conventional iris recognition systems.
The proposed method has three novelties compared to previous works. First, when generating iris feature codes, we excluded detected eyelid, eyelash and SR (specular reflection) regions, which act as noise factors in iris feature extraction. Second, for enrollment, we checked the number of reliable iris feature codes that were not generated from the eyelid, eyelash and SR occluded regions. Only if the number of reliable codes exceeded in the predetermined threshold, we performed enrollment with high confidence, which reduced the FRR (false rejection rate). Third, two Gabor filters were used for local and global iris textures and the HDs calculated by those Gabor filters were fused by the SVM and the consequent authentication error was greatly reduced.
Experimental results showed that the authentication error of the proposed method was much smaller than the authentication errors when using the single Gabor filter, the filter-bank, the score level and the decision level fusion methods.