scholarly journals Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images

2020 ◽  
Vol 6 ◽  
pp. e248 ◽  
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
High Performance ◽  
Personal Data ◽  
Region Of Interest ◽  
Identification System ◽  
Exposure Fusion ◽  
Finger Vein ◽  
Face Images ◽  
Image Contrast Enhancement ◽  
Personal Recognition ◽  
Efficient Matching

In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. Furthermore, the features are extracted using CNNs and dropout approach, after that, the Softmax performs as a recognizer. In conventional fingervein system, the region of interest image contrast enhancement using exposure fusion framework is input into the CNNs model. Moreover, the RF classifier is proposed for classification. In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. The score provided by these systems is combined for improving Human identification. The proposed algorithm is evaluated on publicly available SDUMLA-HMT real multimodal biometric database using a GPU based implementation. Experimental results on the datasets has shown significant capability for identification biometric system. The proposed work can offer an accurate and efficient matching compared with other system based on unimodal, bimodal, multimodal characteristics.

scholarly journals Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3997 ◽  
Author(s):  
Qiong Yao ◽  
Dan Song ◽  
Xiang Xu
Keyword(s):  
Region Of Interest ◽  
Dynamic Threshold ◽  
Identification System ◽  
Horizontal Line ◽  
Threshold Strategy ◽  
Edge Information ◽  
Finger Vein ◽  
Localization Performance ◽  
The Impact ◽  
3Σ Criterion

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 σ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 σ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.

scholarly journals Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier

2020 ◽  
Vol 6 (2) ◽  
pp. 20-26
Author(s):  
Amreen Khan ◽  
Dr. Abhishek Bhatt
Keyword(s):  
Random Forest ◽  
High Performance ◽  
Research Work ◽  
Personal Data ◽  
Recognition System ◽  
Identification System ◽  
Optimum Approach ◽  
Vein Recognition ◽  
Effective Models ◽  
Result Analysis

In recent years, the need for security of personal data is becoming progressively important. A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. With respect to this concern, the identification system based on the fusion of multibiometric values is the most recommended in order to significantly improve and obtain high performance accuracy. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Retinex Algorithm, Stacked Deep Auto Encoder and Random forest (RF) classifier based on multi-biometric fingerprint as well as finger-vein recognition system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. In order to gain above mentioned objectives, fingerprint and fingervein dataset is taken for training and testing. The result analysis shows approx. 97% accuracy, 92% precision rate as well as 0.04 EER that shows enhancement over existing work.

Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

scholarly journals A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

2020 ◽  
Vol 17 (3) ◽  
pp. 1562
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Gabor Filter ◽  
Regression Line ◽  
Histogram Equalization ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Face Images ◽  
Level Method

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis

2021 ◽  
pp. 2250013
Author(s):  
Aldjia Boucetta ◽  
Leila Boussaad
Keyword(s):  
Correlation Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Deep Cnn ◽  
Identification Stage ◽  
Human Finger ◽  
The Right

Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained Deep-CNN models. In this paper, a finger-vein identification system is proposed, which uses Squeezenet pretrained Deep-CNN model as feature extractor from the left and the right finger vein patterns. Then, combines this Deep-based features by using a feature-level Discriminant Correlation Analysis (DCA) to reduce feature dimensions and to give the most relevant features. Finally, these composite feature vectors are used as input data for a Support Vector Machine (SVM) classifier, in an identification stage. This method is tested on two widely available finger vein databases, namely SDUMLA-HMT and FV-USM. Experimental results show that the proposed finger vein identification system achieves significant high mean accuracy rates.

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