Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

Video anomaly detection is a challenging task owing to the rare and diverse nature of abnormal events. However, most of the existing methods only learn the normality in a single space, focusing on low-level detailed features, which is easily affected by unimportant pixels. To address this issue, in this study, we propose a semi-supervised method based on the generative adversarial network and frame prediction, wherein the normality is learned in both the original image space and latent space, and the events deviating from the normality are detected as anomalies. In particular, given a video clip, we first predict a future frame and minimize the prediction errors between the generated frame and its ground truth. Thereafter, we encode the predicted frames and their ground truths in the latent space and minimize their differences. In the testing phase, we calculate the normal scores of each frame in both the image and latent spaces to obtain a comprehensive evaluation. Utilizing the multispace can capture more normality distribution information of the data, which can benefit anomaly detection. The results of experiments on three benchmark datasets demonstrate the effectiveness of the proposed method.

Owing to the rapid development of deep learning and the high efficiency of hashing, hashing methods based on deep learning models have been extensively adopted in the area of cross-modal retrieval. In general, in existing deep model-based methods, modality-specific features play an important role during the hash learning. However, most existing methods only use the modality-specific features from the final fully connected layer, ignoring the semantic relevance among modality-specific features with different scales in multiple layers. To address this issue, in this study, we put forward an end-to-end deep hashing method called deep multiscale fusion hashing (DMFH) for cross-modal retrieval. For the proposed DMFH, we first design different network branches for two modalities and then adopt multiscale fusion models for each branch network to fuse the multiscale semantics, which can be used to explore the semantic relevance. Furthermore, the multi-fusion models also embed the multiscale semantics into the final hash codes, making the final hash codes more representative. In addition, the proposed DMFH can learn common hash codes directly without a relaxation, thereby avoiding a loss in accuracy during hash learning. Experimental results on three benchmark datasets prove the relative superiority of the proposed method.

Image aesthetics assessment has emerged as a hot topic in recent years due to its potential in numerous high-level vision applications. In this paper, distinguished from existing studies relying on a single label, we propose quantifying image aesthetics by a distribution over multiple quality levels. The distribution-based representation characterizes the disagreement among users' aesthetic preferences regarding the same image, and is also compatible with the traditional task of aesthetic label prediction. Our framework is developed based on fully convolutional networks and enables inputs of varying sizes. In this way, we circumvent the fixed-size constraint of prevalent convolutional neural networks, and avoid the risk of impairing the intrinsic aesthetic appeal of images. Moreover, given the fact that aesthetic perceiving is coupled with semantic understanding, we present a novel semantic-aware hybrid NEtwork (SANE), which harvests the information from object categorization and scene recognition to enhance image aesthetics assessment. Experiments on two benchmark datasets have well verified the effectiveness of our approach in both scenarios of aesthetic distribution prediction and aesthetic label prediction, and highlighted the benefits of input preserving as well as semantic understanding for images.

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.