Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face–iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

Iris recognition is recognized as one of the most reliable and efficient technique for human identification in the biometric fields. The Gabor filter and local binary pattern (LBP) are widely adopted for feature extraction in face recognition. However, it is difficult to achieve high recognition accuracy when the Gabor filter or LBP is directly applied to iris texture representation. This paper presents an effective iris feature descriptor, which first uses 2D-Gabor filter to extract multi-orientation imaginary (MOI) feature, and then applies uniform LBP for region feature encoding. Thus, the MOI feature-by-point energy is converted into that of the uniform LBP histogram-by-block, during which the distributions of the intra- and inter-class are greatly widened. Such process largely improves distinguishability of MOI features. Finally, the Bhattacharyya distance is adopted for matching. Experimental results on CASIA and JLU iris image databases show that this method performs better for combining MOI features and LBP encoding as compared to their individual function.

An accurate iris segmentation method is crucial for the iris recognition system. The conventional iris segmentation algorithms have poor adaptability when applied to heterogeneous iris databases. Therefore, researchers have applied deep learning to the field of iris segmentation. A modified U-Net is proposed to perform the iris segmentation for heterogeneous iris databases, referred to as DropBlock and modified shortcut branch U-Net. The main work is as follows: first, EfficientNetV2 based on DropBlock is used as convolutional blocks of U-Net to improve the ability of feature extraction and generalization of the network. Second, an improved shortcut branch structure is proposed for U-Net to reduce the loss of information during the downsampling process. The experimental results on the CASIA-iris-interval-v4, IITD, and UBIRIS.v2 iris databases demonstrate that this method can not only have good versatility but also provide higher accuracy on heterogeneous databases. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories—a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

Iris segmentation is a critical step in the iris recognition system. Since the quality of iris database taken under different camera sensors varies greatly, thus most existing iris segmentation methods are designed for a particular collection device. Meanwhile, many iris segmentation methods based on convolutional neural networks (CNNs) require a lot of computational costs and hardware costs (storage space), which are not suitable for deploying on low‐performance devices. To address the above problems, an accurate and efficient heterogeneous iris segmentation network is proposed in this paper. First, the authors design an efficient feature extraction network, which combines depth‐wise separable convolution with traditional convolution to greatly reduce model parameters and computational cost while maintaining segmentation accuracy. Then, a Multi‐scale Context Information Extraction Module (MCIEM) is proposed to extract multi‐scale spatial information at a more granular level and enhance the discriminability of the iris region. Finally, a Multi‐layer Feature Information Fusion Module (MFIFM) is proposed to reduce the loss of information during the downsampling process. Experimental results on multi‐source heterogeneous iris database show that the proposed network can not only achieve state‐of‐the‐art performance but is also more efficient in terms of required parameters, calculated load, and storage space.

Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris , and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better.