Local Gabor Binary Pattern Histogram Research Articles

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier.

The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others.

Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification

Land Use and Land Cover (LULC) monitoring is crucial for global transformation, sustainable land control, urban planning, urban growth prediction, and the establishment of climate regulations for long-term development. Remote sensing images have become increasingly important in many environmental planning and land use surveys in recent times. LULC is evaluated in this research using the Sat 4, Sat 6, and Eurosat datasets. Various spectral feature bands are involved, but unexpectedly little consideration has been given to these characteristics in deep learning models. Due to the wide availability of RGB models in computer vision, this research mainly utilized RGB bands. Once the pre-processing is carried out for the images of the selected dataset, the hybrid feature extraction is performed using Haralick texture features, an oriented gradient histogram, a local Gabor binary pattern histogram sequence, and Harris Corner Detection to extract features from the images. After that, the Improved Mayfly Optimization (IMO) method is used to choose the optimal features. IMO-based feature selection algorithms have several advantages that include features such as a high learning rate and computational efficiency. After obtaining the optimal feature selection, the LULC classes are classified using a multi-class classifier known as the Multiplicative Long Short-Term Memory (mLSTM) network. The main functionality of the multiplicative LSTM classifier is to recall appropriate information for a comprehensive duration. In order to accomplish an improved result in LULC classification, a higher amount of remote sensing data should be processed. So, the simulation outcomes demonstrated that the proposed IMO-mLSTM efficiently classifies the LULC classes in terms of classification accuracy, recall, and precision. When compared with ConvNet and Alexnet, the proposed IMO-mLSTM method accomplished accuracies of 99.99% on Sat 4, 99.98% on Sat 6, and 98.52% on the Eurosat datasets.

Automatic identification of focus personage in multi-lingual news images

Annotations of character IDs in news images are critical as ground truth for news retrieval and recommendation system. Universality and accuracy optimization of deep neural network models constitutes the key technology to improve the precision and computing efficiency of automatic news character identification, which is attracting increased attention globally. This paper explores the optimized deep neural network model for automatic focus personage identification in multi-lingual news. First, the face model of the focus personage is trained by using the corresponding face images from German news as positive samples. Next, the scheme of Recurrent Convolutional Neural Network (RCNN) + Bi-directional Long-Short Term Memory (Bi-LSTM) + Conditional Random Field (CRF) is utilized to label the focus name, and the RCNN-RCNN encoder–decoder is applied to translate names of people into multiple languages. Third, face features are described by combining the advantages of Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and RCNN, and iterative quantization (ITQ) is used to binarize codes. Finally, a name semantic network is built for different domains. Experiments are performed on a dataset which comprises approximately 100,000 news images. The experimental results demonstrate that the proposed method achieves a significant improvement over other algorithms.

Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images

Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-based particle swarm optimization (PSO) algorithm is applied to select the optimal features, whose benefits are a fast convergence rate and ease of implementation. After selecting the optimal feature values, a long short-term memory (LSTM) network is utilized to classify the LULC classes. Experimental results showed that the human group-based PSO algorithm with a LSTM classifier effectively well differentiates the LULC classes in terms of classification accuracy, recall and precision. A maximum improvement of 6.03% on Sat 4 and 7.17% on Sat 6 in LULC classification is reached when the proposed human group-based PSO with LSTM is compared to individual LSTM, PSO with LSTM, and Human Group Optimization (HGO) with LSTM. Moreover, an improvement of 2.56% in accuracy is achieved, compared to the existing models, GoogleNet, Visual Geometric Group (VGG), AlexNet, ConvNet, when the proposed method is applied.

Leaf Disease Detection Based on Local Gabor Binary Pattern Histogram Sequence and Neural Network

Agriculture forms the main source of food in India, especially in the southern area. The economy of India directly depends on agriculture plants. But due to some major diseases such as blast, brown spot, and bacterial blight, there is a reduction in plant growth which greatly affects agricultural productivity. The farmers add irrelevant pesticides with their limited knowledge which will degrade the quality of the crop but also degrade the soil quality. In the proposed method Machine Vision techniques based on neural networks are used to detect plant health or diseases indicated by leaf anomaly. Image processing algorithms such as K means clustering is used to segment affected areas. From the segmented images of the plant leaf, features are extracted using Color Coherence Vector (CCV) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS). The extracted features are fed as input to a backpropagation neural network to classify the unhealthy leaf

Human age classification using appearance and facial skin ageing features with multi-class support vector machine

Human age classification via face images is not only difficult for human being but also challenging for a machine. But, because of potential applications in the field of computer vision, this topic has attracted attention of many researchers. In this paper, a novel two stage age classification framework based on appearance and facial skin ageing features with multi-class support vector machine (M-SVM) is proposed to classify the face images into seven age groups. Appearance features consist of shape features such as, geometric ratios and face angle and facial skin textural features extracted by using local Gabor binary pattern histogram (LGBPH). Facial skin ageing features consist of facial skin textural features and wrinkle analysis. The proposed age classification framework is trained and tested with face images collected from FG-NET ageing database and PAL face database and achieved greatly improved age classification accuracy of 94.45%.

Door Knob Hand Recognition System

Biometric applications have been used globally in everyday life. However, conventional biometrics is created and optimized for high-security scenarios. Being used in daily life by ordinary untrained people is a new challenge. Facing this challenge, designing a biometric system with prior constraints of ergonomics, we propose ergonomic biometrics design model, which attains the physiological factors, the psychological factors, and the conventional security characteristics. With this model, a novel hand-based biometric system, door knob hand recognition system (DKHRS), is proposed. DKHRS has the identical appearance of a conventional door knob, which is an optimum solution in both physiological factors and psychological factors. In this system, a hand image is captured by door knob imaging scheme, which is a tailored omnivision imaging structure and is optimized for this predetermined door knob appearance. Then features are extracted by local Gabor binary pattern histogram sequence method and classified by projective dictionary pair learning. In the experiment on a large data set including 12 000 images from 200 people, the proposed system achieves competitive recognition performance comparing with conventional biometrics like face and fingerprint recognition systems, with an equal error rate of 0.091%. This paper shows that a biometric system could be built with a reliable recognition performance under the ergonomic constraints.

Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.

Real‐world gender classification via local Gabor binary pattern and three‐dimensional face reconstruction by generic elastic model

In this study, a novel method is proposed for gender classification by adding facial depth features to texture features. Accordingly, the three-dimensional (3D) generic elastic model is used to reconstruct the 3D model from human face using only a single 2D frontal image. Then, the texture and depth are extracted from the reconstructed face model. Afterwards, the local Gabor binary pattern (LGBP) is applied to both facial texture and reconstructed depth to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final LGBP histogram bins are generated and classified by the support vector machine. Favourable outcomes are acquired for gender classification on the labelled faces in the wild and FERET databases based on the proposed method compared to several state-of-the-arts in gender classification.

A kernelized sparsity-based approach for best spectral bands selection for face recognition

We study face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, the robustness of four state-of-the-art algorithms, namely Multi-block Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP), Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and Patterns of Oriented Edge Magnitudes (POEM-WPCA) against high illumination variation is studied. Second, we propose to enhance the performance of the four mentioned algorithms, which has been drastically decreased upon the day lighted face images provided by IRIS-M3 face database. For this purpose, we use visible narrow band subspectral images selected from the mentioned database. We formulate best spectral bands selection as a pursuit optimization problem wherein the vector of weights determining the importance of each visible spectral band is supposed to be sparse, and hence can be determined by minimizing its L1-norm. Several fusing approaches are then applied on selected best spectral bands using multi-scale and multi-orientation Gabor wavelets. The results highlight further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our subspectral images based approach with its two components; bands selection and bands fusion, to increase the accuracy of the studied algorithms by at least 14 % upon the proposed database.

An HOG-LGBPHS human detector with dimensionality reduction by PLS

By combining Histogram of Oriented Gradient (HOG), which is based on evaluating well-normalized local histograms of image gradient orientations in a dense grid, with Local Gabor Binary Pattern Histogram Sequence (LGBPHS), which concatenate the histograms of all the local regions of all the local Gabor magnitude binary pattern maps, as a feature set, we proposed a novel human detection feature. We employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, to project the feature onto a much lower dimensional subspace (9 dimensions, reduced from the original over 12000). We test the new feature in INRIA person dataset by using a linear SVM, and it yields an error rate of 1.35% with a false negatives (FN) rate of 0.46% and a false positive (FP) rate of 0.89%, while the error rate of HOG is 7.11% with a FN rate of 4.09% and a FP rate of 3.02%, and the error rate of LGBPHS is 13.55% with a FN rate of 4.94% and a FP rate of 8.61%.

Hybrid Facial Representations for Emotion Recognition

Automatic facial expression recognition is a widely studied problem in computer vision and human‐robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block‐based descriptor and a pixelbased descriptor. The block‐based descriptor represents the micro‐orientation and micro‐geometric structure information. The pixel‐based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

Efficient face recognition using wavelet-based generalized neural network

This paper presents an efficient face recognition method where enhanced local Gabor binary pattern histogram sequence has been used for efficient face feature extraction and generalized neural network with wavelet as activation function is being used for classification. In this method the face is first decomposed into multiresolution Gabor wavelets the magnitude responses of which are applied to enhanced local binary patterns. The efficiency has been significantly improved by combining two efficient local appearance descriptors named Gabor wavelet and enhanced local binary pattern with generalized neural network. Generalized neural network is a proven technique for pattern recognition and is insensitive to small changes in input data. The proposed method is robust-to-slight variation of imaging conditions and pose variations. Performance comparison with other existing techniques shows that the proposed technique provides better results in terms of false acceptance rate, false rejection rate, equal error rate and time complexity.

Impact of action unit detection in automatic emotion recognition

In this paper, we investigate the interest of action unit (AU) detection for automatic emotion recognition. We propose and compare two emotion detectors: the first works directly on a high-dimensional feature space and the second projects facial image in the low-dimensional space of AU intensities before recognizing emotion. In both approaches, facial images are coded by local Gabor binary pattern (LGBP) histogram differences. These features reduce the sensitivity to subject identity by computing differences between two LGBP histograms: one computed on an expressive image and the other synthesized and approaching the one we would compute on a neutral face of the same subject. As classifiers, we test support vector machines with different kernels. A new kernel is proposed, the histogram difference intersection kernel that increases classification performances. This kernel is well suited when exploiting the proposed histogram differences. Thorough experiments on three challenging databases (respectively, the Cohn-Kanade, MMI and Bosphorus databases) show the accuracy of our AU and emotion detectors. They lead to significant conclusions on three critical issues: (1) the interest of combining different training databases labeled by different AU coders, (2) the influence of each AU according to its type and detection accuracy on emotion recognition and (3) the sensitivity to identity variations.