Fingerprint Image Recognition Research Articles

RETRACTED: Optimal feature reduction for biometric authentication using intelligent computing techniques

The Intelligent Computing area such as Automatic Biometric authentication is an emerging and high priority research work where the researchers invent several biometric applications which result in the revolutionary development in the recent era. In this approach, a novel algorithm is known as Modified AntLion Optimization (MALO) with Multi Kernel Support Vector Machine (MKSVM) was used to classify and recognize the fingerprint, and retina image efficiently. In the early stage of this research, the pre-processing of the biometric images was done for contrast enhancement and it was implemented by histogram equalization technique. Next, features were extracted by Gray Level Co-occurrence Matrix (GLCM), minutiae, Gray Level Run Length Matrix (GLRLM), and Autocorrelation methods. Then the features extracted were reduced by Probabilistic Principal Component Analysis (PPCA) method. Then the feature selection method was employed and the optimal features were attained by applying the Modified AntLion Optimization (MALO) technique. Finally, the machine learning classification technique was executed for categorizing biometric recognition. Here, the machine learning classification technique named Multi Kernel Support Vector Machine (MKSVM) has been used. The performance of the proposed algorithm was analyzed in terms of accuracy, sensitivity, and specificity. Results indicate that the Multi Kernel Support Vector Machine (MKSVM) yields the best accuracy of 91.60% and 90.30% for fingerprint and retina image recognition respectively, yields the sensitivity of 84.70% and 89.41% for fingerprint and retina image recognition, respectively, yields the specificity of 91.30% and 92.70% for fingerprint and retina image recognition respectively.

Fingerprint pre-processing and feature engineering to enhance agricultural products categorization

Among many biometric technologies, fingerprint identification is the earliest, most pervasively applied, and cheapest biometric technology. It seamlessly corporates integrates sensors, biotechnology, electronic technology, digital image processing and pattern recognition. Fingerprint based identity authentication has expanded from the traditional criminal investigation to e-commerce, attendance, access control, credit card, endowment insurance system and other fields, and has become the popular biometric technology. As for the existing matching algorithm such as feature-point-based or image-based and so on, the accuracy and speed of fingerprint automatic identification are far from the satisfaction of many practical applications. In this work, we investigate the fingerprint feature extraction, and optimize the performance of fingerprint login authentication system by introducing a new feature extractor based on binary image through a brief thinning process. Aiming at the problems of low flexibility, high cost and low efficiency of algorithm in embedded chip of current fingerprint identification system, the automatic fingerprint identification system is comprehensively analyzed. Our proposed method can improve the design and optimization of the automatic fingerprint identification system. Our method can improve the accuracy of fingerprint image recognition in the automatic fingerprint identification system. Meanwhile, it can also improve the adaptability and stability of the automatic fingerprint identification system in various domains. Also, our method can substantially improve the feature extraction of agriculture products. Thereby, the classification task can be enhanced accordingly.

Application of Machine Learning in Fingerprint Image Enhancement and Recognition: A Review

Biometric characteristics helps to recognize an individual among others. Each individual has a unique biometric feature. So, an automated system is designed to recognize an individual. In today’s growing AI development, biometric recognition is applied in many security systems. One of oldest and widely used authentic biometric methodology is fingerprint recognition. Many fingerprint recognition algorithms are designed and developed in order to reduce error rate and to improve accuracy. In this paper, a comprehensive review is presented on various techniques used for fingerprint recognition system along with their performance and their limitations. The purpose of this paper is to review various recent work on the fingerprint recognition system, to explain step by step the steps for recognizing fingerprints, and to provide summaries of the fingerprint databases with functionality

Research on fingerprint image recognition based on convolution neural network

Research on fingerprint image recognition based on convolution neural network

Research on fingerprint image recognition based on convolution neural network

In order to overcome the problem of poor image matching performance of the image recognition method, a method of fingerprint image recognition based on convolution neural network is proposed. In this method, the defaced fingerprint image is pre-processed by smoothing, convergence, equalisation, background foreground segmentation and distortion correction, and the feature points of the defaced fingerprint image are extracted by combining the neighbourhood judgment method, and the information pseudo feature points are removed by fusing the feature points, the centre points are extracted from the feature points of the defaced fingerprint image, and the centre block image is identified by convolution neural network, so as to realise the defaced fingerprint image distinguish. The experimental results show that the performance of restoration and reconstruction is improved. The rejection rate (FRR) is 3.75%, the false recognition rate (FAR) is 1.25%, and the correct recognition rate (CR) is 99.25%.

Fingerprints Image Recognition by Using Perceptron Artificial Neural Network

Security systems that use passwords or identity cards can be hacked and misused. One of alternative security system is to use biometric identification. The biometric system that is popularly used is fingerprints, because the system is safe and comfortable. Fingerprints have a distinctive pattern for each individual and this makes fingerprints relatively difficult to fake, so the system is safe. Comfortable because the verification process is easily done. The problem that often occurs on the system of fingerprint scanner is found an error and the user has difficulty when accessing. To handle with these problems has developed an artificial intelligence system. One of arificial intelligence in pattern identification is artificial neural networks (ANN). From some of the results of previous research showed that the ANN method is reliable in pattern identification. Based on these facts, the method used in this research is the perceptron ANN method with values learning rate varying. In the research the program conducted by testing 20 samples showed that the performance of the perceptron ANN method is relatively good method in fingerprint image recognition. This can be indicated from the value of accuracy (0.95), precision (0.83), TP rate (1), and FP rate (0.07)). In addition, the location of the point coordinate (FP rate; TP rate) is (0.07; 1) in ROC graphs is located on the upper left (perfect classifier region).

PENINGKATAN KUALITAS CITRA SIDIK JARI DENGAN ALGORITMA MINUTIAE EXTRACTION DAN LEARNING VECTOR QUANTIZATION

Sistem pengenalan sidik jari bertujuan untuk mengindentifikasi seseorang. Kendala utama dalam pengenalan sidik jari seseorang, adalah karena citranya memiliki kualitas yang rendah. Kualitas tersebut disebabkan oleh jenis kulit yang berminyak, kering, kotor dan jenis scanner fingerprint yang digunakan. Untuk itu dilakukan peningkatan dengan tujuan memperbaiki citra sidik jari sebagai faktor utama penentu tingkat akurasi hasil pengenalan citra sidik jari. Agar citra sidik jari yang tidak jelas mudah diinterpretasi oleh manusia maupun mesin, maka perlu peningkatan kualitas citra tersebut dengan cara memperjelas garis sidik jarinya. Penelitian ini bertujuan untuk peningkatan kualitas citra sidik jari dengan Algoritma Minutiae Extraction dan metode Learning Vector Quantization (LVQ) sebagai pengujian. Pengujian ini dilakukan dengan menggunakan 4 citra sebagai pelatihan pada setiap kelasnya. Adapun jumlah kelas pada pengujian ini seluruhnya 10 kelas dengan tiap kelas terdiri dari 10 citra. Pada pengujian 75 data citra sidik jari diperoleh tingkat akurasi 83.3333%.

Research on partial fingerprint recognition algorithm based on deep learning

Fingerprint recognition technology is widely used as a kind of powerful and effective authentication method on various mobile devices. However, most mobile devices use small-area fingerprint scanners, and these fingerprint scanners can only obtain a part of the user’s fingerprint information. Besides, traditional fingerprint recognition algorithms excessively rely on the details of fingerprints, and their recognition performance has great limitations in mobile devices which can only get partial fingerprint images due to fingerprint scanners. This paper proposes a partial fingerprint recognition algorithm based on deep learning for the recognition of partial fingerprint images. It can improve the structure of convolutional neural networks, use two kinds of loss functions for network training and feature extraction and finally improve the recognition performance of partial fingerprint images. The experimental results show that the fingerprint recognition algorithm in this paper has a better performance than the existing fingerprint recognition algorithm based on deep learning on the problem of partial fingerprint classification and fingerprint recognition in the public dataset NIST-DB4 and self-built dataset NCUT-FR.

English

2 ABSTRACT: Fingerprint image segmentation is part of pre-processing in fingerprint image recognition system. It has a critical effect to the fingerprint image recognition system. Regardless of years of research in the area of general purpose image segmentation, it is still a very challenging task. A new image decomposition scheme, called the Adaptive and Orientation algorithm, is proposed to achieve effective segmentation for fingerprint images in this work. The proposed model is inspired by total variation models, but it differentiates itself by integrating two unique features of fingerprints; namely, scale and orientation. The proposed Adaptive and Orientation algorithm decomposes a fingerprint image into two layers: cartoon and texture. The cartoon layer contains unwanted components (e.g. structured noise) while the texture layer mainly consists of the latent fingerprint. Proposed segmentation algorithm is experimented and analyzed for two different fingerprint images. The PSNR for image segmentation has been used as a comparison parameter for proposed image segmentation methods.

Fingerprint Segmentation Algorithms: A Literature Review

Fingerprint image segmentation is part of pre-processing in fingerprint image recognition system. It has a critical effect to the fingerprint image recognition system. This paper review the different algorithms particularly used for fingerprint segmentation. Adaptive Total Variation Model, Directional Total Variation Model, method based on combination of ridge orientation and frequency features, method based on orientation field information combined with statistical characteristics of gray, Ridge Template Correlation, method using three pixel features(being the coherence, the mean and the variance) are discussed and compared based on their performance and concluded that Adaptive Total Variation Model and Directional Total Variation Model provides better results, so it will be good for the field of researches. General Terms Image recognition, fingerprint image preprocessing, algorithms of fingerprint segmentation.

Fingerprint image enhancement and recognition algorithms: a survey

Fingerprint systems have received a great deal of research and attracted many researchers’ effort since they provide a powerful tool for access control and security and for practical applications. A literature review of the techniques used to extract the features of fingerprint as well as recognition techniques is given in this paper. Some of the reviewed research articles have used traditional methods such as recognition techniques, whereas the other articles have used neural networks methods. In addition, fingerprint techniques of enhancement are introduced.