The randomness of iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex iris image structure and the various sources of intra-class variations result in the difficulty of iris representation. Although, a number of iris recognition methods have been proposed, it has been found that several accurate iris recognition algorithms use multiscale techniques, which provide a well-suited representation for iris recognition. In this paper and after a thorough analysis and summarization, a multiscale edge detection approach has been employed as a pre-processing step to efficiently localize the iris followed by a new feature extraction technique which is based on a combination of some multiscale feature extraction techniques. This combination uses special Gabor filters and wavelet maxima components. Finally, a promising feature vector representation using moment invariants is proposed. This has resulted in a compact and efficient feature vector. In addition, a fast matching scheme based on exclusive OR operation to compute bits similarity is proposed where the result experimentation was carryout out using CASIA database. The experimental results have shown that the proposed system yields attractive performances and could be used for personal identification in an efficient and effective manner and comparable to the best iris recognition algorithm found in the current literature.