The paper presents a method for rapid visual recognition of personal identity based on the statistical independence test of iris texture. The unique texture of the iris, particularly the trabecular meshwork, is analyzed using 2-D Gabor wavelet coefficients, which are encoded into a 256-byte "iris code." Statistical decision theory is applied to generate identification decisions, with a theoretical "cross-over" error rate of one in 131,000. The method is efficient, with a processing time of about one-tenth of a second per iris code, and it achieves high confidence in personal identification. The iris code's entropy and the number of independent degrees-of-freedom are analyzed, showing that the iris texture has significant independent variation, enhancing the uniqueness and reliability of the recognition system. The performance of the system is evaluated using a large database of eye images, demonstrating its effectiveness in recognizing individuals with high accuracy.The paper presents a method for rapid visual recognition of personal identity based on the statistical independence test of iris texture. The unique texture of the iris, particularly the trabecular meshwork, is analyzed using 2-D Gabor wavelet coefficients, which are encoded into a 256-byte "iris code." Statistical decision theory is applied to generate identification decisions, with a theoretical "cross-over" error rate of one in 131,000. The method is efficient, with a processing time of about one-tenth of a second per iris code, and it achieves high confidence in personal identification. The iris code's entropy and the number of independent degrees-of-freedom are analyzed, showing that the iris texture has significant independent variation, enhancing the uniqueness and reliability of the recognition system. The performance of the system is evaluated using a large database of eye images, demonstrating its effectiveness in recognizing individuals with high accuracy.