Iris Feature Extraction Algorithm Using Vertically Expanded Blanket Dimension and Lacunarity
Wei Zhou · 2011
Iris feature extraction is important in iris recognition.An iris feature extraction algorithm is proposed by using combination of blanket dimension and lacunarity.Because Human iris texture is characterized by fractal geometry due to its rich self-similarity and abundant variation,vertically expanded blanket dimension is employed to represent iris texture variation and radial pattern at different resolution levels.Lacunarity is introduced to extract iris features that have different texture and fractal patterns but have same fractal dimension value.The combination of blanket dimension and lacunarity in iris feature extraction can embody the minute change of texture information comprehensively,and improve the capacity of iris classification.The experimental results on the CASIA-IrisV3-Interval iris database show that the combination of blanket dimension and lacunarity can extract iris textural features accurately and effectively,and high performance for iris recognition is achieved by using those features.