Iris Feature Extraction Method Based on Deep Hashing
Xiaolu Yu · 2024
Due to the rapid development of deep learning, the iris feature extraction performance has been significantly enhanced. However, iris features extracted by convolutional neural networks are typically represented as floating-point numbers. This results in degradation in data storage and retrieval performance. To this end, this paper proposes an iris feature extraction method. This method enhances feature discrimination to improve iris recognition capability and utilizes binary hash codes to enhance storage and retrieval efficiency. Firstly, category labels are mapped to hash centers. Subsequently, the feature vectors extracted by the convolutional neural network gradually approach the hash centers under the influence of the loss function, the feature vectors exhibit a clustered distribution in the hash space. Finally, the convolutional neural network converts iris images into binary hash codes for iris recognition. Experiments on two public iris datasets validate the effectiveness of the proposed method in iris verification and quick data storage and retrieval outcomes.