scholarly journals Multimodal Identification Based on Fingerprint and Face Images via a Hetero-Associative Memory Method

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2976
Author(s):  
Qi Han ◽  
Heng Yang ◽  
Tengfei Weng ◽  
Guorong Chen ◽  
Jinyuan Liu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Asymptotic Stability ◽  
Associative Memory ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Face Images ◽  
The Face ◽  
Biometric Information ◽  
The Given ◽  
Biometric Features

Multimodal identification, which exploits biometric information from more than one biometric modality, is more secure and reliable than unimodal identification. Face recognition and fingerprint recognition have received a lot of attention in recent years for their unique advantages. However, how to integrate these two modalities and develop an effective multimodal identification system are still challenging problems. Hetero-associative memory (HAM) models store some patterns that can be reliably retrieved from other patterns in a robust way. Therefore, in this paper, face and fingerprint biometric features are integrated by the use of a hetero-associative memory method for multimodal identification. The proposed multimodal identification system can integrate face and fingerprint biometric features at feature level when the system converges to the state of asymptotic stability. In experiment 1, the predicted fingerprint by inputting an authorized user’s face is compared with the real fingerprint, and the matching rate of each group is higher than the given threshold. In experiment 2 and experiment 3, the predicted fingerprint by inputting the face of an unauthorized user and the stealing authorized user’s face is compared with its real fingerprint input, respectively, and the matching rate of each group is lower than the given threshold. The experimental results prove the feasibility of the proposed multimodal identification system.

scholarly journals Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

2020 ◽  
Vol 1 (1) ◽  
pp. 22-26
Author(s):  
Isnawati Muslihah ◽  
Muqorobin Muqorobin
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Local Binary Pattern ◽  
Face Image ◽  
Image Texture ◽  
Identification System ◽  
Linear Discriminant ◽  
Face Images ◽  
The Face

Face recognition is an identification system that uses the characteristics of a person's face for processing. There is a feature in the face image so that it can be distinguished between one face and another face. One way to recognize face images is to analyze the texture of the face image. Texture analysis generally requires a feature extraction process. In different images, the characteristics will also differ. This characteristic will be the basis for the recognition of facial images. However, existing face recognition methods experience efficiency problems and rely heavily on the extraction of the right features. This study aims to study the texture characteristics of the extraction results using the Local Binary Pattern (LBP) method which is applied to deal with the introduction of Probabilistic Linear Discriminant Analysis (PLDA). The data used in this study are human face images from the AR Faces database, consisting of 136 objects (76 men and 60 women), each of which has 7 types of images Based on the results of testing shows the LBP method can produce the highest accuracy with a value of 95.53% in the introduction of PLDA.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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>

Thermal Human Face Recognition for Biometric Security System

2013 ◽  
pp. 1-24 ◽  
Author(s):  
Ayan Seal ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu
Keyword(s):  
Face Recognition ◽  
Thermal Infrared ◽  
Infrared Images ◽  
Human Machine Interaction ◽  
Human Face Recognition ◽  
Face Images ◽  
Thermal Infrared Images ◽  
The Face ◽  
Intensity Images ◽  
Machine Interaction

Automatic face recognition has been comprehensively studied for more than four decades, since face recognition of individuals has many applications, particularly in human-machine interaction and security. Although face recognition systems have achieved a significant level of maturity with some realistic achievement, face recognition still remains a challenging problem due to large variation in face images. Face recognition techniques can be generally divided into three categories based on the face image acquisition methodology: methods that work on intensity images, those that deal with video sequences, and those that require other sensory (like 3D sensory or infra-red imagery) data. Researchers are using thermal infrared images for face recognition. Since thermal infrared images have some advantages over 2D images. In this chapter, an overview of some of the well-known techniques of face recognition using thermal infrared faces are discussed, and some of the drawbacks and benefits of each of these methods mentioned therein are discussed. This chapter talks about some of the most recent algorithms developed for this purpose, and tries to give a brief idea of the state of the art of face recognition technology. The authors propose one approach for evaluating the performance of face recognition algorithms using thermal infrared images. They also note the results of several classifiers on a benchmark dataset (Terravic Facial Infrared Database).

3D Face Reconstruction from Two Orthogonal Images for Face Recognition Applications

2012 ◽  
pp. 223-239
Author(s):  
Stefano Berretti ◽  
Alberto Del Bimbo ◽  
Pietro Pala
Keyword(s):  
Face Recognition ◽  
Control Points ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Face Reconstruction ◽  
Face Images ◽  
Model Control ◽  
3D Geometry ◽  
The Face ◽  
Manual Assistance

In this paper, an original hybrid 2D-3D face recognition approach is proposed using two orthogonal face images, frontal and side views of the face, to reconstruct the complete 3D geometry of the face. This is obtained using a model based solution, in which a 3D template face model is morphed according to the correspondence of a limited set of control points identified on the frontal and side images in addition to the model. Control points identification is driven by an Active Shape Model applied to the frontal image, whereas subsequent manual assistance is required for control points localization on the side view. The reconstructed 3D model is finally matched, using the iso-geodesic regions approach against a gallery of 3D face scans for the purpose of face recognition. Preliminary experimental results are provided on a small database showing the viability of the approach.

scholarly journals Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals 3D Face Image Inpainting with Generative Adversarial Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tongxin Wei ◽  
Qingbao Li ◽  
Jinjin Liu ◽  
Ping Zhang ◽  
Zhifeng Chen
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Image Inpainting ◽  
Face Image ◽  
Edge Information ◽  
3D Face ◽  
Face Images ◽  
The Face ◽  
Match Effect ◽  
Traditional Image

In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.

scholarly journals Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Rong Wang
Keyword(s):  
Face Recognition ◽  
High Accuracy ◽  
Virtual Training ◽  
Face Images ◽  
Squared Error ◽  
Training Samples ◽  
Real World Applications ◽  
The Face ◽  
Error Classification ◽  
Accuracy Performance

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

scholarly journals Eigenvector Weighting Function in Face Recognition

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Pang Ying Han ◽  
Andrew Teoh Beng Jin ◽  
Lim Heng Siong
Keyword(s):  
Face Recognition ◽  
Weighting Function ◽  
Subspace Learning ◽  
Linear Projection ◽  
Face Images ◽  
Locality Preserving ◽  
Learning Techniques ◽  
Dimensionality Reduction Technique ◽  
The Face ◽  
Face Subspace

Graph-based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra-class variations of the face images. Hence, features which are extracted by the graph-based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF), is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra-class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed technique.

The System of Face Recognition Design

2014 ◽  
Vol 644-650 ◽  
pp. 3943-3946
Author(s):  
Xiao Bin Yu ◽  
Zi Qiao Li ◽  
Wen Qiang Ke ◽  
Rui Peng Li ◽  
Kai Xiong
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Color Space ◽  
Color Model ◽  
Face Images ◽  
Accurate Detection ◽  
Haar Features ◽  
The Face ◽  
The Media ◽  
Skin Color Model

The technology of face recognition is the media to face images as the identity of the face recognition system.Through the choice of color space and the establishment of skin color model, give a rough detection for the human's image, then use the face Haar features getting more accurate detection.

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