scholarly journals Circular Gabor wavelet algorithm for fingerprint liveness detection

2020 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Olufade F.W. Onifade ◽  
Paul Akinde ◽  
Folasade Olubusola Isinkaye
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Distinct Difference ◽  
Identification Algorithm ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Total Internal Reflectance ◽  
Internal Reflectance ◽  
Wavelet Algorithm

Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.   

scholarly journals Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sulayman Ahmed ◽  
Mondher Frikha ◽  
Taha Darwassh Hanawy Hussein ◽  
Javad Rahebi
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Swarm Optimization ◽  
Gabor Wavelet Transform ◽  
Face Recognition System

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

Study of Fingerprint Image Preprocessing Technology

2014 ◽  
Vol 519-520 ◽  
pp. 581-584
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Recognition System ◽  
Gabor Wavelet ◽  
Fingerprint Image ◽  
Image Preprocessing ◽  
Identity Recognition ◽  
Background Segmentation ◽  
Image Thinning ◽  
Segmentation Image ◽  
Test Result ◽  
Wavelet Algorithm

Fingerprint image preprocessing technology has been widely used with the development of intelligent identity recognition system, which studies fingerprint image restoration, enhancement and thinning based on digital image processing technology. This paper presents system design and realization of preprocessing technology for fingerprint image. Fingerprint image has restoration processing by adopting image filed calculation, image background segmentation, image equalization, image convergence and image smoothing algorithm in order at first. Then Gabor wavelet algorithm is used to enhance the contrast of fingerprint image. Image thinning algorithm is carried out to extract the skeleton of fingerprint image at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Effect of Eyelid and Eyelash Occlusions on a Practical Iris Recognition System: Analysis and Solution

2015 ◽  
Vol 29 (08) ◽  
pp. 1556016 ◽  
Author(s):  
Raed T. Al-Zubi ◽  
Khalid A. Darabkh ◽  
Nayel Al-Zubi
Keyword(s):  
Feature Extraction ◽  
Small Area ◽  
Iris Recognition ◽  
Hamming Distance ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Traditional Methods ◽  
Image Capturing ◽  
Extraction Processes ◽  
Fixed Region

One of the crucial and inherent issues in a practical iris recognition system is the occlusion that happens due to eyelids and eyelashes. This occlusion increases the complexity and degrades the performance of matching and feature extraction processes. Generally, two types of approaches have been proposed to solve this issue. The first approach requires generating an iris mask that indicates which part of the iris is useful and which others are occluded. However, in the second approach, a fixed region of interest (ROI) within the iris area is selected to avoid the regions of occlusion. In this paper, we experimentally study both approaches but due to the latter characteristic, which is its ability to simplify the matching and feature extraction processes, it has been adopted in our techniques used, specifically for iris segmentation, iris normalization, and feature extraction. Accordingly, for matching and feature extraction, the lower side of the pupillary region (i.e. the innermost 25% of the lower half of the iris ring) is found to be the best ROI. This small area of iris is almost free of eyelids and eyelashes and it contains abundant texture information. Interestingly, this selection of small area helps us in proposing a simple yet efficient technique for feature extraction, called mean-based feature extraction technique (MB-FET). This technique is based on analyzing the local intensity variations. The proposed technique achieves a lower processing burden than other traditional methods such as Fourier or wavelet decompositions (e.g. Gabor wavelet). In most traditional techniques, many parameters (e.g. five parameters for 2D-Gabor filter) must be optimally determined in advance to achieve an accurate feature extraction process. Unfortunately, these parameters may not match various variations in image capturing conditions (e.g. variations in illumination due to change in image capturing distance). Moreover, the basic functions of the traditional methods are fixed in advance (off-line) and do not necessarily match the texture of all irises in the database. However, for our proposed technique MB-FET, there is no need to determine in advance any parameter or basic function. MB-FET dynamically adapts its parameter (only one parameter) with intensity variations. The proposed technique generates a binary iris code, hence a simple and fast matching process is done using the Hamming distance. The experimental results using the CASIA iris database show that the proposed technique achieves promising results for a robust and reliable iris recognition.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

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