Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR) light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN); which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II) training database (selected from the UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE) dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.

https://www.mdpi.com/2073-8994/9/11/263/pdf

Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment

Relationships between lean and sustainable construction: Positive impacts of lean practices over sustainability during construction phase

Self-Organising Fuzzy Logic Classifier

The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accep...

Facial-recognition algorithms: A literature review:

Unconstrained face recognition is still an open problem as the state-of-the-art algorithms have not yet reached high recognition performance in real-world environments. This paper addresses this problem by proposing a new approach called sparse fingerprint classification algorithm (SFCA). In the training phase, for each enrolled subject, a grid of patches is extracted from each subject’s face images in order to construct representative dictionaries. In the testing phase, a grid is extracted from the query image and every patch is transformed into a binary sparse representation using the dictionary, creating a fingerprint of the face. The binary coefficients vote for their corresponding classes and the maximum-vote class decides the identity of the query image. Experiments were carried out on seven widely-used face databases. The results demonstrate that when the size of the data set is small or medium ( e.g. , the number of subjects is not greater than one hundred), SFCA is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size, and distance from the camera than other current state-of-the-art algorithms.

Face Recognition Using Sparse Fingerprint Classification Algorithm

The purpose of this study was the development of a theoretical framework integrating concepts of lean construction, sustainability and the life cycle of buildings. It was prepared using a systematic review of the literature through the Prisma method. Findings show that there is scarce scientific literature about lean construction contributions to activities related to the consumption phase of the life cycle of a building. Project and construction activities presented the highest number of lean construction contributions in the production phase. Research limitations are terms used in the search sentences and the choice of surveyed databases. The study contributes with knowledge that can be used by companies to adopt lean construction. It is also useful for researchers who can explore several research lines. The originality of this research is to view in a same table the interrelationship of concepts about lean construction, sustainability and the life cycle of buildings.

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Integration of concepts about lean construction, sustainability and life cycle of buildings: a literature review

Analytical and Machine Learning-based approaches to estimate the steady-state temperature limit on the surface of Cu powder beds when heated by a concentrated laser energy source

Tomás Vivanco Larrain

Papers

Face Recognition Using Sparse Fingerprint Classification Algorithm

Integration of concepts about lean construction, sustainability and life cycle of buildings: a literature review

Analytical and Machine Learning-based approaches to estimate the steady-state temperature limit on the surface of Cu powder beds when heated by a concentrated laser energy source