Advances in computer vision (CV) have led to an increasing market for biometric recognition systems. However, as more users are registered in a system, its expanding dataset will increase the system's response time and lower its recognition stability. As mentioned above, we propose a new high-performance algorithm suitable for embedded finger-vein recognition systems. First, the semantic segmentation based on DeepLabv 3+ filters out the background noise and enhances processing stability. The adaptive symmetric mask-based discrete wavelet transform (A-SMDWT) and adaptive image contrast enhancement were used in the preprocessing of images, and feature extraction was performed through the repeated line tracking (RLT) method. Next, the histogram of oriented gradient (HOG) of the image was computed, after which a support vector machine (SVM) was then used to train a classifier. Finally, a self-established finger-vein image dataset as well as a public dataset was implemented in the Raspberry Pi platform, which is a low-level embedded system. The experimental results indicated that the proposed system offers advantages such as a high accuracy rate, low device cost, and fast response time. Therefore, the three major issues that were encountered in previous embedded finger-vein image verification systems were mitigated in this work.
PurposeThe purpose of this study, we present a robot used in education. Influenced by the epoch of revolutionary digital technology, the methodology of education has gone boundless. The robot programming sustainability and ability to solve problems is one an important skill that coding students require to learn programming. This educational have been integrated into curriculum instruction in clubs.Design/methodology/approachRobotics education has been regarded as a potential approach to enhance students' Science, technology, engineering, and mathematics learning competencies. The popular platform of robots diversifies educational practices by its advantages of reorganizational and logical forms. In this paper, we focus on the effects of applying blended instructional approaches to robot education on students' programming sustainability and ability.FindingsThe students of department of mechanical engineering at the University in Taipei city, who participate elective educational robot courses, prove through surveys that the problem-based leaning method with robot programming can effectively enhance students' interests and learning motivations in learning new knowledge and promote students' designing skills for a sustainable society.Originality/valueIn this paper, the authors focus on the effects of applying blended instructional approaches to robot education on students' programming sustainability and ability.
In this study, we propose a new solution based on Adaboost algorithm and Back Propagation Network (BPN) of Neural Network (NN) combining local and global features with cascade architecture to detect human faces. We use Modified Census Transform (MCT) feature that belong to texture features and is less sensitive to illumination for local feature calculation. By this approach, it is not necessary to preprocess each sub-window of the image. For classification, we use the structure of hierarchical feature to control the number of features. With only MCT, it is easy to misjudge faces, and therefore in this work we include the brightness information of global features to eliminate the false positive regions. As a result, the proposed approach can have Detection Rate (DR) of 99%, false positives of only 11, and detection speed of 27.92 Frame Per Second (FPS).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.