关键词: 虹膜识别, 折叠分块, 周期延拓, 小波变换, 过零检测, BP神经网络

Abstract: In the extraction of iris features, most methods directly perform some transformation on the iris-normalized enhanced images. To reduce the iris feature dimension and ensure the recognition accuracy, this paper proposes the method of normalizing iris radial folding block and circumferential cycle block and then carrying out haar wavelet transform, which reduces the sensitivity of the iris region to noise and ensures that effective iris features are not neutralized while reducing the iris feature dimension. In order to further overcome the sensitivity of the iris recognition to rotation, this paper uses a periodic wavelet transform to extract high frequency information. Finally, using the classification method of BP（Back Propagation） neural network, the high-frequency information after wavelet transform is directly used as the input of the classifier, which further improves the accuracy of iris recognition. Experiments show that the number of feature points can be as low as 120 and the correct recognition rate can reach 99.48% by using the method proposed in this paper.

Key words: iris recognition, folding block, periodic extension, wavelet transform, zero-crossing detection, BP neural network