Constructing irislet: A new wavelet type which matched for iris image characteristics

Iris has a unique pattern that can be used in biometric recognition. To extract the features of the iris, it can be done based on the textural characteristics of the iris pattern. One method is a texture-based feature extraction using wavelet. To construct a wavelet type which matched for a signal, in this case two-dimensional signal from the iris image, the necessary steps are quite complex. In this research, all stages of wavelet design are carried out, beginning from iris image data acquisition up to the finding of the new wavelet, which will then be referred to as irislet. There are 19 (nineteen) steps in the design of this wavelet. To do all the stages, several basic concepts are required: convolution, circular Hough transform, conversion into unwrapped polar image form, determining the profile of the 1-D line images, signal averaging, concept of Daubechies wavelet basis, calculating signal energy, least squares method, how to construct scaling and wavelet functions, as well as the cascade algorithm. The test results showed that the recognition implementation irislet shows recognition rate is 100% correct.