We propose a novel palm vein recognition scheme based on an adaptive 2D Gabor filter. Three key steps were studied in this scheme: region of interest (ROI) extraction, adaptive Gabor filtering, and template matching. First, in the palm vein image extraction step, the authors used the index finger on both sides of the valley to locate the square area, and then iteratively expanded the area of the square box to maximise the ROI. Second, in the feature extraction step, a novel parameter selection scheme was proposed for optimising the Gabor filter. Third, in the template matching step, the author presented a novel template matching algorithm referred to as the minimum normalised Hamming distance. Experimental results demonstrated that the scheme achieved good performance with an EER of 0.12%.

Designing a new dynamic and optimal scheme for face–iris fusion based on the score level, feature level and decision level fusion is considered in this study. Prior to implementing the proposed combined level fusion, several schemes are separately implemented at each level of fusion to investigate the performance improvement of each level of fusion on face and iris modalities. In fact, the optimum scheme is constructed by selecting flexible and dynamic features and scores of face and iris biometrics and then combining the advantages of different levels of fusion. Consequently, the scheme produces a set of fast and flexible features and scores for fusion. On the other hand, the idea of threshold-optimised decisions is used in this study to fuse the optimised decisions of face and iris biometrics. Experimental results on verification rates demonstrate a significant improvement of proposed combined level fusion scheme over unimodal and multimodal fusion methods.

A new algorithm for reconstructing the fingerprint super-resolution (SR) image is presented. The basic idea of the algorithm is to reconstruct the SR image by using sparse representation with ridge pattern prior based on classification coupled dictionaries. First, the orientations of training patches are estimated by the weighted linear projection analysis. In the second procedure, the qualities of patches are assessed by the coherence of point orientations, the training patches are subsequently classified into eight groups based on their own orientations and qualities, and then the training patches of each class are selected from candidate patches by their own quality and the corresponding classification coupled dictionaries are learned. In the end, single SR fingerprint is reconstructed using sparse representation with ridge pattern by classification coupled dictionaries. The experiments with the database of FVC2000, FVC2004 and FVC 2006 are carried out using various SR reconstruction methods. The experiments show that the proposed method achieves better results in comparison with other methods and will help to improve the performance of automatic fingerprint identification system.

Combining multiple human trait features is a proven and effective strategy for biometric-based personal identification. In this study, the authors investigate the fusion of two biometric modalities, i.e. ear and palmprint, at feature-level. Ear and palmprint patterns are characterised by a rich and stable structure, which provides a large amount of information to discriminate individuals. Local texture descriptors, namely local binary patterns, weber local descriptor, and binarised statistical image features, were used to extract the discriminant features for robust human identification. The authors’ extensive experimental analysis based on the benchmark IIT Delhi-2 ear and IIT Delhi palmprint databases confirmed that the proposed multimodal biometric system is able to increase recognition rates compared with that produced by single-modal biometrics, attaining a recognition rate of 100%.

In this study, a framework to super resolve and recognise the long range captured iris polar images is proposed. The proposed framework consists of best frame selection algorithm, modified diamond search algorithm, Gaussian process regression (GPR) based and enhanced iterated back projection (EIBP)-based super-resolution approach, fuzzy entropy-based feature selector and neural network (NN) classifier. The framework uses linear kernel co-variance function in local patch-based GPR and EIBP algorithms and it super resolves the iris images depending on the contents of the patches, without an external dataset. NN classifier classifies the iris images by using features extracted using discrete cosine transform domain based no-reference image quality assessment model, Gray level co-occurrence matrix, Hu seven moments and statistical features. The framework is tested using CASIA long range iris database by comparing and analysing the peak signal-to-noise ratio, structural similarity index matrix and visual information fidelity in pixel domain of proposed algorithms with Yang and Nguyen framework. The results demonstrate that the proposed framework is well suited for recognition of iris images captured at a long distance.