Due to the lack of pre-judgment of fingerprints, fingerprint authentication systems are frequently vulnerable to artificial replicas. Anonymous people can impersonate authorized users to complete various authentication operations, thereby disrupting the order of life and causing tremendous economic losses to society. Therefore, to ensure that authorized users’ fingerprint information is not used illegally, one possible anti-spoofing technique, called fingerprint liveness detection (FLD), has been exploited. Compared with hand-crafted feature methods, deep convolutional neural network (DCNN) can automatically learn high-level semantic detail via supervised learning algorithm without any professional background knowledge. However, one disadvantage of most CNNs models is that fixed scale images (e.g., 227 × 227) are essential in the input layer. Although the scale problem can be handled by cropping or scaling operations via transforming an image of any scale into a fixed scale, they can easily cause some key texture information loss and image resolution degradation, which will weaken the generalization performance of classifier model. In this paper, a novel fingerprint liveness detection method, called an improved Deep Convolutional Neural Network with Image Scale Equalization (DCNNISE), has been proposed to preserve texture information and maintain image resolution. Besides, an adaptive learning rate method has been used in this paper. In performance evaluation, the confusion matrix is applied into FLD for the first time as a performance indicator. Amounts of experimental results based on LivDet 2011 and LivDet 2013 data sets also verify that the detection performance of our method is superior to other methods.

Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA.

Nowadays, fingerprint recognition technology has aroused wide attention in the society, especially in the application of identity authentication with smart phone as the carrier. However, the disadvantage of these devices is that the identification sensors are vulnerable to spoofing attacks from artificial replicas made from clay, gelatin, silicon, etc. To resolve it, a feasible anti-deception countermeasure, called fingerprint liveness detection (FLD), has been proposed. Different from most shallow feature methods, the deep convolutional neural network (DCNN) based FLD methods have been widely explored with the properties of fast operation, few parameters and end-to-end feature self-learning. But DCNN is confronted with a contradictory problem, on the one hand, the testing or training result will keep rising with the increasement of multilayer perceptron (MLP), finally tends to a stable value, and continue to increase MLP results will decline. Much research, on the other hand, shows that the number of MLP is the foundation of realizing a high performance detection. Hereby, we apply Deep Residual Network (DRN) to FLD for the first time to solve the contradiction mentioned in this paper. Next, to eliminate the interference of invalid regions of given images, a region of interest (ROI) extraction algorithm has been put forward. Afterwards, to avoid the parameters learned plunging into local optimization, an adaptive learning based deep residual networks (ALDRN), which automatically adjust learning rate if those monitoring parameters (verification accuracy) are stable, has been explored. Finally, we propose a novel texture enhancement based on local gradient pattern (LGP) method to improve the generalization of model classifier as well. Experimental results on three benchmarks Datasets: LivDet 2011, 2013 and 2015, show that our results outperform the state-of-the-art FLD methods.

Biometrics spoofing attack (BsSA) frequently occurs when an adversary impersonates a lawful user to access to the biometric system by means of some forged or synthetic samples, especially in fingerprint or face authentication. In allusion to the problem above, the mainstream countermeasure, called biometrics liveness detection (BLD), is raised. In this article, we propose a more robust and accurate BLD strategy by taking advantage of weighted multimodal convolutional neural networks (MCNNs) to extract diverse deep features. Before detection, the ROI operation first is performed to remove those invalid backgrounds of fingerprints. Then, a multimodal feature fusion strategy is proposed to make full use of the learning capacity of convolutional neural networks (CNNs) without human interactions. It is well known that characteristics of the different direct splicing together and for the subsequent classification is unreasonable, thus, a weighted summation strategy is explored. More specifically, we assign each type of feature weight contribution rate, sum them, and then learn the optimal combination of different model features. In the final detection phase, to verify our proposed algorithm with higher accuracy, detailed analyses of the fingerprint evaluations on intradatabase, cross-material, and cross-database, respectively, which also include assessment under the fusion of different modal features, and face evaluations on the same-database, are evaluated. Experimental results on several benchmark data sets LivDet 2011, 2013, 2015, and NUAA demonstrate that our approach achieves outstanding results in fingerprint intradatabase and cross-material evaluations as well as face anti-spoofing evaluations comparing with previous methods. Most importantly, our method is more accurate and robust than other existing fingerprint anti-spoofing methods when evaluating cross-database.