Multimodal Biometric Authentication for Smartphones

Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

Best Regression for Eye Recognition

Human eye offers a number of opportunities for biometric recognition. The essential parts of the eye like cornea, iris, veins and retina can determine different characteristics. Systems using eyes’ features are widely deployed for identification in government requirement levels and laws; but also beginning to have more space in portable validation world. The first image was prepared to be used and monitored using CLAHE which means (Contrast Limited Adaptive Histogram Equalization) to improve the contrast of the image, after that the 3D surface plot was created for this image then different types of regression were used and the better one was chosen. The results showed that power regression is better, and fitter than other fitting methods (8th, 7th, 6th, 5th, 4th, 3rd, 2nd) degree polynomial, and straight line respectively, when depending on the sum of residual squared. The estimations of R-square demonstrated that (5th, 6th, 7th, 8th) have a great proportion of variance in the model followed by (power, 4th, 3rd, 2nd, straight line) respectively. The conclusion from these results is that the power regression has a better fitting than other types of fitting functions for this study and similar ones.

Dangerous goods identification based on multi-channel neural network

In the current society, there is an increasing demand for dangerous goods identification technology in X-ray images, but at the current stage, most of the identification of dangerous goods in X-ray images still relies on artificial eye recognition. In order to solve this problem, this paper proposes A method for automatically and intelligently identifying dangerous goods in X-ray images based on the transformation of the convolutional neural network. By adding multi-channel convolution and normalization to the convolutional neural network, the target features are extracted to achieve automatic detection of dangerous goods. The purpose of better identification. In the simulation experiment, using the public data set and self-built data set in the X-ray security inspection field, the accuracy of the identification of dangerous goods in the X-ray image was obtained more satisfactory results than the traditional dangerous goods identification. The improved Alex Net network’ s testing accuracy on contraband knives and guns is 8.53% and 11.6% higher than the training accuracy of the original Alex Net network.

Enzyvitands: versatile evolvable colorimetric chembioreceptors

Designing the perfect sensor is the dream of any chemist. Since decades, a wide diversity of synthetic receptors targeting analytes has been explored in chemistry. Their chemical optimization is hard and with no guarantee of success. In this context, we propose a fast and self assembling colorimetric bio-chemical receptor coined Enzyvitand. It consists only of commercial chemicals. It relies on the reunification of combinatorial chemistry , first and second coordination spheres interactions and indicators displacement assays. All harbored within a protein cavity. The sensor is highly modular, cheap and evolvable. Thanks to its solved X-ray structure, we rationally designed it for the selectiv naked-eye recognition of dopamine over other neutrotransmitters through second coordination sphere. Hence, our sensor imitates a biological receptor for the recognition of neurotransmitters. Finally, it works in complex samples such as urine. Its immediate high versatility and evolvability is valuable for the selective detection of a wide assortment of analytes from small molecules up to micro-organisms. For the future, we anticipate new biotechnological or immunotherapeutic applications of our synthetic oligomer.

Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning

Glaucoma is a domineering and irretrievable neurodegenerative eye disease produced by the optical nerve head owed to extended intra-ocular stress inside the eye. Recognition of glaucoma is an essential job for ophthalmologists. In this paper, we propose a methodology to classify fundus images into normal and glaucoma categories. The proposed approach makes use of image denoising of digital fundus images by utilizing a non-Gaussian bivariate probability distribution function to model the statistics of wavelet coefficients of glaucoma images. The traditional image features were extracted followed by the popular feature selection algorithm. The selected features are then fed to the least square support vector machine classifier employing various kernel functions. The comparison result shows that the proposed approach offers maximum classification accuracy of nearly 91.22% over the existing best approaches.

Enzyvitands: evolvable biosynthetic and colorimetric chalices

<div><div><div><p>Designing the perfect sensor is the dream of any chemist. Since decades, a wide diversity of chemosensors targeting analytes has been explored in chemistry. Their chemical optimization is hard and with no guarantee of success. In this context, we propose a fast and easy-to-assemble colorimetric bio-chemical receptor coined Enzyvitand. It consists only of commercial chemicals. It relies on the reunification of combinatorial chemistry, first and second coordination spheres interactions and indicators displacement assays. All harbored within a protein cavity. The sensor is highly modular, cheap and evolvable. Thanks to its solved X-ray structure, we rationally designed it for the naked-eye recognition of dopamine. Hence, our sensor imitates a biological receptor for the recognition of neurotransmitters. Its immediate high versatility and evolvability is valuable for the selective detection of a wide assortment of analytes from small molecules up to micro-organisms. For the future, we anticipate new biotechnological or immunotherapeutic applications of our bio-sensor.</p></div></div></div>

EYE RECOGNITION SYSTEM TO PREVENT ACCIDENTS ON THE ROAD

Today, possibilities of artificial intelligence allow us to see the emergence of autonomous cars. However, there are still many problems in this area at present. Often, such vehicles are “too slow to think”, are not able to reliably process data from video cameras in the event of reflections, glare, and there are also questions about the safety of such driving in difficult weather conditions or in heavy traffic. At the same time, the human factor plays a major role in accidents of driven vehicles. Many accidents involve driver fatigue, distraction, or even falling asleep. At the same time, it is potentially possible to monitor the state of a person behind the wheel by a video sequence received from a camera installed in the car's interior and registering the driver's face in video sequence. In this paper, the existing databases of images of faces and eyes are considered, and an algorithm is presented that detects the state of closed eyes based on Haar detectors and convolutional neural networks.

Construction of a Stable Lanthanide Metal-Organic Framework as a Luminescent Probe for Rapid Naked-Eye Recognition of Fe3+ and Acetone

Four lanthanide metal-organic frameworks (Ln-MOFs), namely {[Me2NH2][LnL]·2H2O}n (Ln = Eu 1, Tb 2, Dy 3, Gd 4), have been constructed from a new tetradentate ligand 1-(3,5-dicarboxylatobenzyl)-3,5-pyrazole dicarboxylic acid (H4L). These isostructural Ln-MOFs, crystallizing in the monoclinic P21/c space group, feature a 3D structure with 7.5 Å × 9.8 Å channels along the b axis and the point symbol of {410.614.84} {45.6}2. The framework shows high air and hydrolytic stability, which can keep stable after exposed to humid air for 30 days or immersed in water for seven days. Four MOFs with different lanthanide ions (Eu3+, Tb3+, Dy3+, and Gd3+) ions exhibit red, green, yellow, and blue emissions, respectively. The Tb-MOF emitting bright green luminescence can selectively and rapidly (<40 s) detect Fe3+ in aqueous media via a fluorescence quenching effect. The detection shows excellent anti-inference ability toward many other cations and can be easily recognized by naked eyes. In addition, it can also be utilized as a rapid fluorescent sensor to detect acetone solvent as well as acetone vapor. Similar results of sensing experiments were observed from Eu-MOF. The sensing mechanism are further discussed.