Biometric passport security based on face recognition and biometric encryption using secure force algorithm into the HCC2D code

In this paper, face recognition technology is studied and explored, and the classical local binary pattern (LBP) algorithm is studied and improved. An improved LBP algorithm is proposed to solve the deficiency of LBP: a "double circle" LBP algorithm is proposed, which improves the rotation invariability of LBP to a greater extent and has a good recognition effect on small-pose face images. On the basis of good recognition effect of LBP based on partitioning, the idea of "multiple partitioning + intermediate partitioning" is put forward, which effectively makes up for the shortage of incomplete extraction of peripheral information of original segmentation line. Facial features are the key features of face recognition. Different organs contribute differently to face image recognition. The idea of "key block" based on the original block is proposed. Finally, the application of an LBP algorithm for face recognition is taken as an example, and the experimental results verify the reliability of the face recognition method based on LBP for weak light and expression change images.

Studies on two-factor authentication based on RFID and face recognition have been carried out on a large scale. However, these studies didn't discuss the way to overcome the weaknesses of face recognition authentication in the access control systems. In this study, two authentication factors, RFID and face recognition, were implemented using the LBP (Local Binary Pattern) algorithm to overcome weaknesses of face recognition authentication in the access control system. Based on the results of performance testing, the access control system has 100% RFID authentication and 80% face recognition authentication. The average time for the RFID authentication process is 0.03 seconds, the face recognition process is 6.3885 seconds and the verification of the face recognition is 0.1970 seconds. The access control system can still work properly after three days without being switched off. The results of security testing showed that the capabilities spoofing detection has 100% overcome the photo attack.

Based on neural network and local binary pattern algorithm, this paper builds a lightweight artificial face recognition system on chip Firefly-RK3399, with high speed, strong robustness and high recognition accuracy. Our embedded artificial intelligent face recognition system mainly consists of face detection, feature extraction and recognition. Multi-task convolutional neural network (MTCNN) under the CaffeOnACL framework is utilized for face detection, and the local binary pattern (LBP) is applied as face recognition algorithm. Experiments illustrate that our artificial intelligent embedded face recognition system has high speed and accuracy, which is easy-carrying and of high commercial value as well.

Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition software in security applications has driven the development of interesting approaches in this field. A large quantity of research in face recognition deals with visible face images. In the visible spectrum the illumination and face expressions changes represent a significant challenge for the recognition system. To avoid these problems, researchers proposed recently the use of 3D and infrared imaging for face recognition. In this work, we introduce a new framework for infrared face recognition using texture descriptors. This framework exploits linear and non linear dimensionality reduction techniques for face learning and recognition in the texture space. Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and Laval University Multispectral Database (Visible, NIR, MWIR, LWIR). The obtained results show high increase in recognition performance when texture descriptors like LBP (Local Binary Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtained in the short wave infrared spectrum (SWIR) using non linear dimensionality reduction techniques.

In the modern digital world, biometric systems are a significant part of personal identification and access control. The use of sensitive biometric data such as iris and facial features raises huge privacy concerns because of the possibilities of data breaches and misuse. Therefore, this project focuses on the enhancement of the security of multimodal biometric systems through encryption mechanisms. The idea is to generate a strong biometric key based on features produced from iris and face biometric systems through advanced machine learning techniques. The features are then employed to produce a strong biometric key capable of encrypting the secret value using the AES algorithm. AES is a widely adopted symmetric encryption algorithm that ensures high efficiency in its software implementation and seamless processing capabilities of image data. This provides not only the reinforcement of authentication but also maintains confidentiality of biometric characteristics and mitigates the risks of privacy attacks and unauthorized access. Thus, this multimodal biometric encryption improves reliability, robustness, and the resistance power of the system against potential attacks, making it a promising step forward in the realm of secure biometric authenticationsystems. Keywords- Biometric encryption,Multimodal biometrics, ,Privacy attacks,Advanced Encryption Standard (AES), Biometric key generation,Iris recognition,Face recognition,Machine learning,Bio-crypto systems, Data security.

Call for Papers: Special Issue ofComputer Vision and Image Understandingon the Analysis of Volumetric Images

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment. Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns (LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

Facial recognition has been used in several areas such as security, detection of criminals or suspects, etc. The face is considered the most important part of the human body in the person identification process since it can be considered the most important perspective in human identification. This paper proposed a real-time monitoring security system for high secured areas rather than relying on human security officers. The proposed system engages with capturing and detecting the human face using the real-time image segment captured by the web camera through the Viola-Jones algorithm. After detecting the face in the captured image, face identification can be made through the LBP by finding the similarities. Once the identification is made, automatically it recognizes the face. Open Computer Vision (OpenCV) is one of the most efficient machine learning libraries used for Object Detection. OpenCV library is used for detection, and Haar - Cascades are trained to detect certain objects. LBP is used for the face recognition process.

A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image

Face recognition is a process of verifying an individual using facial images and it is widely employed in identifying people on social media platforms, validating identity at ATMs, finding missing persons, controlling access to sensitive areas, finding lost pets, etc. Face recognition is still a trending research area because of various challenges like illumination variations, different poses, and expressions of the person. Here, a novel methodology is introduced for face recognition using Histogram of Oriented Gradients (HOG), histogram of Local Binary Patterns (LBP), and Convolutional Neural Network (CNN). The features from HOG, histogram of LBP, and deep features from the proposed CNN are linearly concatenated to produce the feature space and then classified by Support Vector Machine. The face databases ORL, Extended Yale B, and CMUPIE are used for experimental work and attained a recognition rate of 98.48%, 97.33%, and 97.28% respectively.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

Biometric technology is considered as more effective technology for identification and recognition of individuals in most secure environments. Face recognition is a technique based on physiological biometric characteristics. In this paper, we propose Face Recognition using DT-CWT and LBP Features (FRDL) for different databases. The original face image is resized to uniform dimensions of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sup> . The five levels Dual-Tree Complex Wavelet Transform (DT-CWT) is applied on face image to obtain DT-CWT coefficients. The matrix of DT-CWT coefficients is segmented in to 3×3 matrixes. The Local Binary Pattern (LBP) algorithm is applied on each 3×3 matrix to get final features. The Euclidean Distance (ED) is used to compare features of test face image with data base images. It is observed that the values of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are better in the proposed model compare to existing method.

To detect defects in fabrics more efficiently, easily and accurately, a method based on Local Binary Pattern (LBP) is proposed in this paper. The main purpose of this algorithm is to extract the feature value of fabric images. Firstly the feature of the whole defect-free fabric image is got with LBP algorithm. Then the image is divided into small detection windows, and the feature of each window can be obtained. Compare their similarity calculated by Chi-square function to get the threshold. Then process the defective images according to the same procedure. At last compare the similarity with the threshold to obtain defect regions. The defects are detected at the same time. Experimental results demonstrate that, LBP algorithm is effective in the area of detecting defects of fabrics.

Facial recognition using Haar cascade and LBP classifiers