This article presents an efficient fingerprint identification system that implements an initial classification for search-space reduction followed by minutiae neighbor-based feature encoding and matching. The current state-of-the-art fingerprint classification methods use a deep convolutional neural network (DCNN) to assign confidence for the classification prediction, and based on this prediction, the input fingerprint is matched with only the subset of the database that belongs to the predicted class. It can be observed for the DCNNs that as the architectures deepen, the farthest layers of the network learn more abstract information from the input images that result in higher prediction accuracies. However, the downside is that the DCNNs are data hungry and require lots of annotated (labeled) data to learn generalized network parameters for deeper layers. In this article, a shallow multifeature view CNN (SMV-CNN) fingerprint classifier is proposed that extracts: 1) fine-grained features from the input image and 2) abstract features from explicitly derived representations obtained from the input image. The multifeature views are fed to a fully connected neural network (NN) to compute a global classification prediction. The classification results show that the SMV-CNN demonstrated an improvement of 2.8% when compared to baseline CNN consisting of a single grayscale view on an open-source database. Moreover, in comparison with the state-of-the-art residual network (ResNet-50) image classification model, the proposed method performs comparably while being less complex and more efficient during training. The result of classification-based fingerprint identification has shown that the search space is reduced by over 50% without degradation of identification accuracies.

Palmprint‐based human authentication has shown great potential for civil, forensic, and corporate security applications in recent years. Palmprint recognition systems suffer because of large palmprint sizes and the presence of a large number of creases and erroneous minutiae that make the enhancement and matching phases a challenge. In this study, a novel approach is presented based on efficient enhancement and a two‐stage matching technique that demonstrates highly accurate identification results. The enhancement approach extracts minutia features from high‐quality regions based on local ridge characteristics. The selected minutiae are then matched using a two‐stage local and global minutiae neighbour‐based matching technique. To demonstrate the performance of the proposed technique, comparisons with open‐source algorithms are made based on equal error rate and detection error trade‐off graph. The results confirm the efficacy of proposed palmprint enhancement and identification technique.

Fingerprint classification is vital for reducing the search time and computational complexity of the fingerprint identification system. The robustness of classifier relies on the strength of extracted features and the ability to deal with low‐quality fingerprints. The proficiency to learn accurate features from raw fingerprint images rather than explicit feature extraction makes deep convolutional neural networks (DCNNs) attractive for fingerprint classification. The DCNNs use softmax for quantifying model confidence of a class for an input fingerprint image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with the available training examples. The primary goal of this study is to improve the efficacy of the fingerprint classification by dealing with false positives by employing Bayesian model uncertainty. The efficacy of the proposed method is shown through experimentations on NIST special database 4 (NIST‐4) and fingerprint verification competition 2002 database 1‐A (FVC DB1‐A) 2002 and 2004 datasets. Results show that 0.8–1.0% of accuracy is improved with model uncertainty over the conventional DCNN.

Search engines and social networks are two entirely different data sources that can provide valuable information about Influenza. While search engine hosts can deliver popular queries (or terms) used for searching the Influenza related information, the social networks contain useful links of information sources that people have found valuable. The authors hypothesize that such data sources can provide vital first-hand information. In this article, they have proposed a methodology for detecting the information sources from social networks, particularly Twitter. The data filtering and source finding tasks are posed as classification tasks. Search engine queries are used for extracting related dataset. Results have shown that propose approach can be beneficial for extracting useful information regarding side effects, medications and to track geographical location of epidemics affected area.