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Fingerprint enhancement by FFT. Enhanced image (left), Original image (right) 4. FEATURES EXTRACTION The extraction process of fingerprint code is presented below:
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The fingerprints are unique to each individual; they can be used as a means to distinguish one individual from another.Therefore they are used to identify a person. Fingerprint Classification is done to associate a given fingerprint to one of the existing classes, such as left loop, right loop, arch, tented arch and whorl. Classifying fingerprint i...
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... The next important step to extract information from an image is to suppress or handle unwanted distortion or noise in images for further processing [13]. Hence, [13][14][15][16][17][18] used enhancement in image processing to remove noise in their image data. This enhancement intensifies the quality of images which reduce noise and blur, adjust the contrast and enlightening the image details. ...
Exploring the new method of using technology for classifying rice grain quality is pertinent for rice producers in order to provide quality grains and protect consumers from any contamination exist. This is even more important when in today’s market we can see that rice with low quality is traded without stringent quality control which at the end will affect consumer’s health. This paper will review classification methods in determining quality white rice grain. Although there are many researchers developing new process to do rice classification by using different technique, there are still more advanced technique that can be used to do classification. This paper will focus on classifying rice grain quality using artificial neural network (ANN) approach with the help of image processing to identify the impurities contained in the rice grains. The findings show ANN using BPNN has the highest accuracy of 96%, it is also noted that other methods provide equally better performance. This review indicate hybrid method in ANN should be explored next for future work.
... According to Idriss Tazight et al. [10], 2014 in their study entitled "Fingerprint Classification Using Fuzzy-neural Network and Other Methods", they show the fingerprint image processing up until fingerprint classification. In fingerprint classification, they differentiate four methods and those are KNN, Neural Network, Fuzzy Neural Network, and SVM. ...
... For instance, banks have used it for registration and issuing of Bank Verification Numbers (BVN) to their customers; mobile network service providers also use it to register SIM cards for subscribers; it also finds application in security for both residential and commercial buildings. In India and other developed countries, the electronic voting machines based on finger print are being used generaaly for state elections since 1999 up to date [6]. ...
Managing people and keeping adequate record of the attendance for most of the tertiary institutions are difficult task. Moreover, the manual taking of attendance adds to the difficulty of this task because it is strenuous, time intensive and can easily be falsified. Meanwhile, With the advent of new technology, a new world of authentication and security has been created via biometrics. This work aims at improving Covenant Universities paper-based attendance by using biometrics, specifically fingerprint technology. It is a web application that works with a fingerprint reader from which the features of the student’s fingerprint are extracted during enrolment. This is also needed during the authentication phase which occurs during the roll call attendance in the halls of residence. It is implemented using PHP and MySQL.
... Handwriting is image so that could use some methods toward other image like finger print as biometric analysis [10], [11]. The research study using a neural network with multilayer perceptron. ...
Graphology is scientific method to evaluation personality and emotion condition through handwriting and signature. There are many features to identify personality so that previous researches made handwriting analysis automatically. There are page margins, spacing, baseline, vertical zone, font size, slant, pen pressure, and the type t letter. While other studies used features of signature. As image, the analysis of graphology is divided into two approaches that graphics features and segmentation digit each character. This research integrated both of approach to identify personality of handwriting. It used speed and the type of a, d, i m, t letters as features using structure analysis and artificial neural networks. Type of letter recognition was done after character segmentation. Wavelet transform was used to improve recognition. The proposed methods could be used to identify personality of handwriting. Identification of speed feature using structure analysis toward page margin, spacing between lines, and spacing between words that gave 81% accuracy. While identification of unique letters using neural network with multilayer perceptron architecture, which gave 74% accuracy. Variations training data greatly affect recognition. Keywords— speed and unique letter features, handwriting identification; personality; wavelet extraction; multilayer perceptron.
... Handwriting is image so that could use some methods toward other image like finger print as biometric analysis [10], [11]. The research study using a neural network with multilayer perceptron. ...
