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

A face tracking algorithm based on LBP (Local Binary Pattern) histograms and particle filtering is presented in this paper. The mean shift algorithm is adopted to locate the face region coarsely. Then the particles can be propagated efficiently with small random noise added. Since color histograms as face representation can be disturbed easily by the background with skin color. LBP histograms are used as the representation of faces. In order to alleviate the influence of light variance, the face template is updated under certain condition. The experimental results show that the proposed algorithm is superior to the CAMSHIFT algorithm and the particle filter based on color histograms.

In ground-based remote sensing cloud image observation, images with the highest possible resolution are captured to obtain sufficient information about clouds. However, when features are extracted and classification is performed on the basis of the original images, a high-resolution probably means a high (or even more, unacceptable) computation cost. In practical application, a simple and commonly adopted method is to appropriately resize the original image to a version with a decreased resolution. An inevitable problem is whether useful information is lost in this resizing operation. This paper demonstrates that information loss is inevitable and poor classification results may be obtained from the analysis of local binary pattern (LBP) histogram features. However, this problem has been always neglected in previous studies, and the original image is arbitrarily resized without any criterion. In particular, the histogram features based on LBPs actually reflect the distribution of features. Thus, a criterion based on the Kullback–Leibler divergence between LBP histograms from the original and resized images and a penalty term imposed on the resolution are proposed to select the resolution of the resized image. The optimal resolution of the resized image can be selected by minimizing this criterion. Furthermore, experiments based on three ground-based remote sensing cloud image data sets with different original resolutions validate this criterion by analyzing the LBP histogram features.

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 have been widely used for human identification, including fingerprint, face and iris because they cannot be easily duplicated. Recently, biometrics have been also used for animal (i.e. cattle in this paper) identification. Individual cattle identification is necessary for many important reasons including determining legal ownership, verifying transferred source, and implementing disease surveillance and control. Popular traditional methods for individual cattle identification are using plastic ear tags or microchips. However, the tag can be deduplicated and it can be dangerous and takes time for the human expert to place the microchip in the cattle. Also, it may hurt the cattle. Thus, in this paper, muzzle print is used as a biometric for automatic cattle identification. The fusion of texture features extracted from the muzzle image is used to represent individual cattle. They are Gabor feature and Local Binary Pattern (LBP) histogram. Gabor features were extracted at the different scales and orientations in specific frequencies, while LBP histogram was extracted for each local sub-image to preserve local spatial textures. Then, Support Vector Machine (SVM) is employed as a classifier. The proposed method is reported with the perfect accuracy.

Achieving low recognition time and high accuracy in real-time face recognition is challenging. This study implements Viola-Jones, Local Binary Pattern Histogram, and Euclidean Distance for real-time face recognition and calculates the face detection time. The face image is detected using the Viola-Jones method; its features are extracted using the Local Binary Pattern Histogram, and the face is recognized using Euclidean Distance. This study processes sample images from 1013 students as training data, with 20 images represent each student. The experiments show that 268 of 342 testing data are recognized correctly, resulting in an accuracy of 78.4%, with average real-time recognition time of 0.93 seconds.

Combination the corresponding advantage of infrared and visible images, the fusion of visible-light and near-infrared features is becoming one of the key research directions of uncontrolled face recognition. In this paper, a new face fusion recognition method based on near-infrared and visible-light imaging is proposed to make full use of the complementarity in two modalities. Firstly, for near infrared imaging, the low frequency part is retained for discrete cosine transform (DCT) coefficients and the block histogram of local binary pattern (LBP) is jointly applied to represent the discriminative features. Then, LBP features from visible faces are gotten to make up the incapable of local representation in near-infrared imaging. Finally, the three features are sent to the novel two-state fusion module to leverage the performance of face recognition. The experimental results show that the combination of DCT and LBP can extract the complementary features from infrared and visible images, and the two-stage fusion strategy lifts the robustness of the multi-modality face recognition. Especially, the recognition rate of the proposed fusion recognition algorithm has been improved significantly, with regard to the other methods based on statistical feature fusion under the circumstance of small samples.

The initial concept behind the system is to create a secure and powerful smart voting mechanism that incorporates the use of face recognition, the algorithm used being Local Binary Pattern Histogram (LBPH) and Aadhar identification, and use of OTPs to authenticate voters. The main idea behind this is to modernize the voting process removing the errors in it that were made manually, stop fraud in identity identification and allow participating in the process regardless of location. The LBPH which has a reputation of being simple and efficient in real-time face recognition transforms the facial images into local binary patterns subsequently lending them to the creation of histograms which depict features of distinct faces. The training phase involves taking several faces of registered voters in case of different lighting conditions and pose conditions. These pictures are scanned to black and white, they are pre-processed and marked with respective voter identities. The voting is then done in the trained LBPH model to authenticate the voter upon real-time webcam feedback where the system detects the face process it and match it with the trained database. Upon successful verification of the face, the system further cross verifies the Aadhar details of the voter and sends him a one-time password (OTP) to his/her recorded phone number. The voter is then allowed into the candidate panel after the successful multi-factor authentication, where he or she votes. Votes are hash stored in a blockchain where each confirmed vote is hashed with the secure hash, SHA-256, providing the requirement of vote recording that is hashing, traceable, and immutable. Such a multi-tiered solution does not only provide a better class of security and transparency, but also fosters accessibility in long-distance voters, thus the proposed system is a strong and highly scalable answer to the current democratic procedures.

This paper presents a system to recognize face by a variation of LBPH. We use a method of regression of local binary features to get the landmark of face image whose computational complexity is very low. We utilize these landmark points which can be trained to align the face, to extract the facial features. By calculating the Local Binary Patterns Histogram (LBPH) of these landmark points and its neighborhood pixels, we can extract effective facial feature to realize face recognition. This method can increase the calculating speed of LBPH and also can improve the recognition rate. Finally, we show the experimental results using this method to recognize face.

Based on fitting the Local Binary Patterns (LBP) histogram into the bag-of-words paradigm, we propose an LBP variant termed Principal Local Binary Patterns (PLBP) which are learned in an unsupervised way from the data. The learning problem turns out to be the same as the Principal Component Analysis (PCA) and thus can be solved very efficiently. Unlike the manually specified patterns in LBP which distribute very non-uniformly, the learned patterns in PLBP can adapt with the distribution of the data so that they distribute very uniformly, which preserves more information than LBP in the binary coding process. Moreover, PLBP can take advantage of much larger neighborhood than LBP to describe the point, which provides more information. Therefore, PLBP contains more information than LBP to discriminate different classes. The experimental results of face recognition on the FERET and LFW datasets clearly confirm the discrimination power and robustness of PLBP. It achieves very competing performance on both datasets and it is very simple and efficient to compute.

This paper presents a combination techniques of appearance-based and feature-based feature extraction on the T-Zone face area to improve the recognition performance. This study shows that the T-Zone area and the combined technique provides a significant impact on the face recognition rate. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. High recognition rates are obtained when compared to other face recognition methods of the same class. Our result shows of 7% improvement compared with PCA and 2% improvement compare with Sub-Holistic PCA. Our studies proves that the T-Zone area which is consisting of eyes and nose region is a ‘significant facial region’, and we also show that LBP can easily be combined with PCA to reduce the length of the feature vector, while the recognition performance is improved.

Local Binary Patterns (LBP) have been well exploited for facial image analysis recently. In the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. However, not all bins in the LBP histogram are necessary to be useful for facial representation. In this paper, we propose to learn discriminative LBP-Histogram (LBPH) bins for the task of facial expression recognition. Our experiments illustrate that the selected LBPH bins provide a compact and discriminative facial representation. We experimentally illustrate that it is necessary to consider multiscale LBP for representing faces, and most discriminative information is contained in uniform patterns. By adopting SVM with the selected multiscale LBPH bins, we obtain the best recognition performance of 93.1% on the Cohn-Kanade database.

The conventional local binary pattern (LBP) based facial recognition method selects Chi square statistic as the dissimilarity measure for LBP histogram.In view of Chi square statistic's complexity and the high-dimensional recognition process,the conventional method is very slow as recognizing on large-scale face database.A method of LBP histogram projection (LBPHP) was proposed,which projects LBP histogram onto locality preserving projection (LPP) space and obtains low-dimensional LBPHP feature.Recognizing new sample only needs to compare its LBPHP feature with those of training samples.The process is simple and carried on low-dimensional space,thus the proposed method is fast and has good accuracy in the light of powerful discriminative property of LPP.Comparative experiments on two large-scale face databases demonstrated that the LBPHP method is superior to the conventional method on recognition speed.The LBPHP method is prominent especially on large-scale face database and suitable for practical application,e.g.identity authentication.

To enhance the security and authentication processes, this extension integrates Elliptic Curve Cryptography (ECC) for image encryption and Local Binary Pattern Histogram (LBPH) for live face authentication during registration and login. ECC, known for its high security with smaller key sizes, is incorporated to securely encrypt images, addressing the growing need for efficient and lightweight image encryption in resource-constrained environments. By employing ECC, the encryption algorithm ensures that visual data transmitted and stored remains confidential, with reduced computational overhead compared to traditional encryption methods like AES. Additionally, LBPH is employed for real-time face authentication, allowing users to authenticate themselves via facial recognition during both registration and login. This biometric system leverages LBPH’s robustness against variations in lighting and facial expressions, ensuring reliable face recognition in diverse environments. By the integrity of user access control, offering a combining ECC for image encryption and LBPH for face authentication, the extended system strengthens both the confidentiality of transmitted data and seamless and secure authentication process. The enhanced system was evaluated for speed, accuracy, and efficiency, showing significant improvements in real-time security applications.

In this work, our objective is to study the impact of statistical hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60, 90 and 120 meters. Next, the selected statistical analysis test is used to find the statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the statistical analysis reveals which descriptors result in higher recognition performance. Finally, statistical hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of testing FR accuracy only on the female Asian class) when employing database stratification.

Face recognition is an existing and one of the most prominent biometrics techniques, including the processing of images. It is widely used in many applications. The performance of such systems is directly due to face image quality. Since blur and motion blur are common imagery problems, this paper explores the influence of such disturbances on color face recognition performance. The research described in this paper compares the performance of the face recognition algorithm based on the Haar features and Local Binary Patterns Histograms when it uses color face images of good quality, images with added Gaussian blur and motion blur, as well as enhanced images.