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In this study, a new method is presented to fabricate spiral shape single fiber. The micro-needle array (40 μm hole diameter, 80 μm outer diameter, and 100 μm height) is utilized instead of the needle to reduce the diameter of fibers which are electrospun from 23Wt% PVP concentration. In order to have fine and bead-free fibers, the structural parameters of the micro-needle array and space which close microneedles act as an individual one are simulated. The Wet and Dry Etching techniques are used for fabrication of micro-needles. The experimental setup consists of the stepper motor and micrometer head as the pump, distance controller, and voltage generator. The single fiber can be electrospun by applying 1-3 kV bias within 1 mm gap between micro-needle and collector. Using nonconductive collector causes the formation of spiral type single fiber instead of agglomerate fiber. Elastic and expulsion forces in charged fibers seem to be the main reasons of fiber separation and spiral shape formation. The spiral shape fiber is made without traditional lithography techniques like direct patterning or contact exposure which are more expensive and time-consuming. The alteration in the fiber pattern can be seen by changing the applied voltage and spinneret, microneedle and needle. After various experiments, spirality pattern electrospun by microneedle with 10-15 μm and 15-35 μm distribution area of first and second circles and 500-570 nm and 570-660 nm diameter of first and second circles is gained as the structure with minimum distribution area and fiber width. This structure is created when the applied voltage and distance between microneedle and collector are 1kV and 1 mm.

Face recognition has attracted tremendous attention during the last three decades because it is considered a simple pattern recognition and image analysis method. Also, many facial recognition patterns have been introduced and used over the years. The SVM algorithm has been one of the successful models in this field. In this article, we have introduced the special faces first. In the following, we have fully explained the SVM method and its subsets, including linear and non-linear support vector machines. Suggestions for improving the recognition percentage of a person's identity check system by applying the SVM method on the face image using special faces are presented. For this test, 10 face images of 40 people (400 face images in total) have been selected from the ORL database. In this way, by choosing the optimal parameter C, determining the most suitable training samples, comparing more accurately with training images and using the distance with the closest training sample instead of the average distance, the proposed method has been implemented and tested on the famous ORL database. The obtained results are FAR=0.23% and FRR=0.48%, which shows the very high accuracy of the operation following the application of the above suggestions.