The rapid growth of fingerprint authentication-based applications makes presentation attack detection, which is the detection of fake fingerprints, become a crucial problem. There have been numerous attempts to deal with this problem; however, the existing algorithms have a significant trade-off between accuracy and computational complexity. This paper proposes a presentation attack detection method using Convolutional Neural Networks (CNN), named fPADnet (fingerprint Presentation Attack Detection network), which consists of Fire and Gram-K modules. Fire modules of fPADnet are designed following the structure of the SqueezeNet Fire module. Gram-K modules, which are derived from the Gram matrix, are used to extract texture information since texture can provide useful features in distinguishing between real and fake fingerprints. Combining Fire and Gram-K modules results in a compact and efficient network for fake fingerprint detection. Experimental results on three public databases, including LivDet 2011, 2013 and 2015, show that fPADnet can achieve an average detection error rate of 2.61%, which is comparable to the state-of-the-art accuracy, while the network size and processing time are significantly reduced.

With the rapid growth of organic light-emitting diode (OLED) display devices, the industrial manufacturing of OLED panels is currently an expanding global reality. Regarding quality control, automatic defect detection and classification are undoubtedly indispensable. Although defect detection systems have been widely considered in the literature, classification systems have not received appropriate attention. This study proposes the design of an efficient and high-performance system for defect classification by combining well-known machine-learning algorithms: support vector machine, random forest (RF), and k-nearest neighbours. To begin, possible features are designed and feature selection using principal component analysis and RF is investigated to automatically select the most effective features. Then, a hierarchical structure of classifiers is proposed for efficiently adjusting the rates of true defect and fake defect classification. The proposed system is evaluated over a database of 3502 images captured from real OLED display devices in different illumination conditions. The defects in the database are divided into 10 classes corresponding to the types of true defect and fake defect. The experiments confirm that the proposed system can achieve an accuracy of up to 94.0% for the binary classification of true defect and fake defect and an overall recognition rate of 86.3% for the 10 sub-classes.

This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for the segregation of robust line candidates from the pool of initial distortion line segments. A novel straightness cost constraint with a cross-entropy loss was imposed on the selected line candidates, thereby exploiting that novel loss to optimize the lens-distortion parameters using the Levenberg–Marquardt (LM) optimization approach. The best-fit distortion parameters are used for the undistortion of an image frame, thereby employing various high-end vision-based tasks on the distortion-rectified frame. In this study, an investigation was carried out on experimental approaches such as parameter sharing between multiple camera systems and model-specific empirical $\gamma$ -residual rectification factor. The quantitative comparisons were carried out between the proposed method and traditional OpenCV method as well as contemporary state-of-the-art self-calibration techniques on KITTI dataset with synthetically generated distortion ranges. Famous image consistency metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and position error in salient points estimation were employed for the performance evaluations. Finally, for a better performance validation of the proposed system on a real-time ADAS platform, a pragmatic approach of qualitative analysis has been conducted through streamlining high-end vision-based tasks such as object detection, localization, and mapping, and auto-parking on undistorted frames.

User-based collaborative filtering often considers a set of users who rated on a target item and computes similarities between other users and the target user to select his/her neighbors, then extrapolates the target user’s rating from the neighbors’ ratings. This traditional approach uses only the neighbors’ ratings for recommendation measurement. However, according to our study, dissimilar users whose ratings still significantly influence to the target user’s rating prediction. In addition, to choose a video to watch, a user often takes in to consideration multi criteria. We analyze users’ behavior to choose a video. They often explore genres or tags, then read abstraction before choosing a video to watch. Therefore, their ratings and the information of a video have a strong correlation. Therefore, based on the fuzzy neural network, a new collaborative filtering method for video recommendation is proposed. Here, the fuzzy neural network is used to learn users’ ratings with respect to their behaviors. The proposal here is to adjust a model of the neural network with input is users’ behavior and output is their ratings for each target video. Concretely, the behavior of a user (or user profile ) is learned by the users’ ratings and the information of the corresponding videos. In addition, for each target video, all users’ profile who made ratings on it will be collected. Then each profile is treated as an input of the fuzzy neural network and the corresponding rating value is treated as output of the fuzzy neural network. The rating of a user on the target video will be predicted based on the trained neural network. The experiments with netflix dataset reveals that the proposed method is a significantly effective approach.

With the aim of developing an automatic system to verify if facial images meet ISO/IEC 19794-5 standards and ICAO requirements, this paper proposes a robust image processing method to obtain information for quality evaluation step. Specifically, an adaptive background segmentation and a robust facial feature extraction (including eye centers, four lip features and chin) are proposed. In background segmentation, background information is provided after applying an edge-based segmentation. A color-based segmentation is then used to deal with shadows. In order to overcome the influence of head poses and illumination, which are the main factors of unsuccessful eye detection, an improvement of the circular filter-based eye detection is used to locate eye centers. To accurately detect lip features and chin regardless expressions and presences of beards or mustaches, the proposed method fuses edge and color information together. An experiment was performed on a subset of the FERET and GTAV database, and the experimental results demonstrated the accuracy and robustness of the proposed method.