This paper proposes a novel feature descriptor using Optimized Orthogonal Wavelets and Local Tetra Patterns OOWLTrP. Texture features are extracted by using Local Tetra Pattern (LTrP) and wavelet features are extracted through optimized orthogonal wavelet. Eye image from various databases, such as CASIA, MMUI, and UBRIS are first preprocessed to remove salt and pepper noise. Later, the iris is segmented from the rest of the eye image. Features are then extracted by using the proposed method, which combines the goodness of local patterns and orthogonal wavelets. Feed Forward Back Propagation Neural Network (FFBNN) is used for classification of images. During training, the weights of FFBNN are optimized using the Adaptive Central Force Optimization (ACFO). The well trained FFBNN-ACFO is further used for classification of iris images. It has been observed that, there is considerable improvement in accuracy and validation time of the system. The increase in accuracy is due to the fact that, LTrP extracts information from four directions as compared to LBP (Local Binary Pattern), LDP (Local Derivative Pattern), and LTP (Local Ternary Pattern). Coefficients of the orthogonal wavelet are optimization by genetic operators, this adds to the improvement in accuracy and reduction in validation time.