The comparison of iris recognition using principal component analysis, independent component analysis and Gabor wavelets

The iris recognition is a kind of the biometrics technologies based on the physiological characteristics of human body, compared with the feature recognition based on the fingerprint, palm-print, face and sound etc, the iris has some advantages such as uniqueness, stability, high recognition rate, and non-infringing etc. As we known, the traditional iris recognition is using Gabor wavelets features; the iris recognition is performed by a 256-byte iris code, which is computed by applying the Gabor wavelets to a given area of the iris. And the techniques like principal component analysis (PCA) and independent component analysis (ICA) can produce spatially global features. Therefore in this paper we compare the feature extraction algorithm based on PCA, ICA and Gabor wavelets for a compact iris code. We use these methods to generate optimal basis elements which could represent iris signals efficiently. In practice the coefficient of these methods are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing individual´s iris patterns. At last, we did experiment to find the best experimental results.