Analysis of Methods and Technologies of Human Face Recognition

The object of research is the processes of biometric identification and human authentication based on the image of his face for computer vision systems. One of the most problematic places in biometric identification systems using computer vision is the problem of eliminating ambiguity of «scanning». Such ambiguity arises when designing three-dimensional objects of the real world on flat images.In the course of the research, the results of the analysis of the effects of requirements and factors on the features and characteristics of the object of the biometric face recognition system are used. First of all, it is the variability of visual images, the design of three-dimensional objects, the number and location of light sources, the color and intensity of radiation, shadows or reflections from surrounding objects. The solution to the problem of detecting objects on the image lies in the correct choice of the description of objects, for the detection and recognition of which the system is created.Analysis of the features of classes and the properties of face recognition tasks shows that it is sufficient for a database of authentication systems to store a small set of predefined key characteristics, as much as possible characterize the images. Thus, by configuring the system to reduce the probability of incorrect identification, it is possible to use several images belonging to one person. For such purposes, a video sequence of certain specific head movements and facial muscles of the face is sufficient.A generalized algorithm for automatic face detection and recognition is developed. The presented scheme of the generalized algorithm consists of nine simple steps and takes into account the identification features using photo and video images. The advantage of the algorithm is the simplicity of implementation, it allows already at the design stage of the identification system, to quickly evaluate the system's operability by analyzing the internal interaction of its elements

Development of Technique for Face Detection in Image Based on Binarization, Scaling and Segmentation Methods

A technique for face detection in the image is proposed, which is based on binarization, scaling, and segmentation of the image, followed by the determination of the largest connected component that matches the image of the face.Modern methods of binarization, scaling, and taxonomic image segmentation have one or more of the following disadvantages: they have a high computational complexity; require the determination of parameter values. Taxonomic image segmentation methods may have additional disadvantages: they do not allow noise and outliers selection; clusters can't have different shapes and sizes, and their number is fixed.Due to this, to improve the efficiency of face detection techniques, the methods of binarization, scaling and taxonomic segmentation needs to be improved.A binarization method is proposed, the distinction of which is the use of the image background. This allows to simplify the process of scaling and segmentation (since all the pixels in the background are represented by the same color), non-uniform brightness of the face, and not to use the threshold settings and additional parameters.A binary image scaling method is proposed, the distinction of which is the use of an arithmetic mean filter with threshold processing and Fast wavelet transform. This allows to speed up the image segmentation process by about P2 times, where P is the scaling parameter, and not to use the time-consuming procedure for determining.A binary scaled image segmentation method is proposed, the distinction of which is the use of density clustering. This allows to separate areas of the face of non-uniform brightness from the image background, noise and outliers. It also allows clusters to have different shapes and sizes, to not require setting the number of clusters and additional parameters.To determine the scaling parameter, numerous studies were conducted in this work, which concluded that the dependence of the segmentation time on the scaling parameter is close to exponential. It was also found that for small P, where P is the scaling parameter, the quality of face detection deteriorates slightly.The proposed technique for face detection in image based on binarization, scaling and segmentation can be used in intelligent computer systems for biometric identification of a person by the face imag

Amperometric L-arginine biosensor based on a novel recombinant arginine deiminase

The authors describe an amperometric biosensor for the amino acid L-arginine (L-Arg). It is based on the use of a Nafion/Polyaniline (PANi) composite on a platinum screen-printed electrode (Pt-SPE) using a novel recombinant arginine deiminase isolated from Mycoplasma hominis. The protein was over-expressed, purified and employed as a biorecognition element of the sensor. Enzymatic hydrolysis of L-Arg leads to the formation of ammonium ions which diffuse into the Nafion/PANi layer and induce the electroreduction of PANi at a potential of -0.35 V (vs Ag/AgCl). L-Arg sensitivity is 684 +/- 32 A.M-1.m(-2), and the apparent Michaelis-Menten constant K-M(app)) is 0.31 +/- 0.05 mM. The calibration plot is linear over the range 3-200 mu M L-Arg, the limit of detection is 1 mu M, and the response time (for 90% of the total signal change to occur) is 15 s. The sensor is selective and exhibits good storage stability (amp;gt; 1 month without loss in signal). The biosensor was applied to the analysis of L-Arg in pharmaceutical samples and of ammonium and L-Arg in spiked human plasma obtained from blood of healthy volunteers and those with a hepatic disorder. Data generated were found to be in good agreement with a reference fluorometric enzymatic assay.Funding Agencies|European Community [PIRSES-GA-2012-318053]; NATO Science for Peace (SFP) [CBP.NUKR.SFPP 984173]</p