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Multi-biometric systems have been widely accepted in various applications due to its capability to solve the limitations of unimodal systems. Directly storing the biometric templates into a centralised server leads to privacy concerns. In the past few years, many biometric authentication systems based on homomorphic encryption have been introduced to provide security for the templates. Most of the existing solutions rely on an implication of the assumption that the server is 'honest-but-curious'. Therefore, the compromise of server results into the entire system vulnerability and fails to provide the integrity. To address this, we propose a novel multi-instance iris authentication system, BMIAE to deal with malicious attacks over the transmission channel and at the untrusted server. BMIAE encrypt the iris templates using ElGamal encryption to guarantee confidentiality and Smart contract running on a Blockchain helps to achieve the integrity of templates and matching result. BMIAE also addresses the limitations of using Blockchain for biometrics like privacy and expensive storage. To check the effectiveness and robustness, BMIAE has experimented on CASIA-V3-Interval, IITD and SDUMLA-HMT iris databases. Experimental results show that BMIAE provides improved accuracy, and eliminates the need to trust the centralised server when compared to the state-of-the-art approaches.

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The present paper proposes a method of texture classification based on long linear patterns. Linear patterns of long size are bright features defined by morphological properties: linearity, connectivity, width and by a specific Gaussian-like profile whose curvature varies smoothly along the crest line. The most significant information of a texture often appears in the occurrence of grain components. That’s why the present paper used sum of occurrence of grain components for feature extraction. The features are constructed from the different combination of long linear patterns with different orientations. These features offer a better discriminating strategy for texture classification. Further, the distance function captured from the sum of occurrence of grain components of textures is expected to enhance the class seperability power. The class seperability power of these features is investigated in the classification experiments with arbitrarily chosen texture images taken from the Brodatz album. The experimental results indicated good analysis, and how the classification of textures will be effected with different long linear patterns

An innovative image retrieval agenda by concatenating deep learning features from GoogleNet and low‐level features from HSI and RGB color space is proposed in this article. Most of the CNN features suffer from loss of information due to image resize as a pre‐processing stage. To reduce this information loss super‐resolution technic is used for resizing images. An improved form of dot‐diffused block truncation coding is used for extracting RGB handcraft features. To discover the interdependencies between color and intensity component of an image, interchannel voting between hue, saturation, and intensity component is calculated as a color feature in HSI space. Histogram of orientated gradient (HOG) feature is used as shape feature. Five standard performance parameters, average precision rate, average recall rate, F‐Measure, Average Normalized Modified Retrieval Rank, and Total Minimum Retrieval Epoch, are applied on nine image datasets: Corel‐1K, Corel‐5K, Corel‐10K, VisTex, STex, ColorBrodatz and three subsets of ImageNet dataset for evaluation process of proposed method. For all dataset the best performance is achieved by the proposed method with respect to all performance parameters.

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