3D facial expression recognition algorithm using local threshold binary pattern and histogram of oriented gradient

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

In this paper we present a system which is focused on developing algorithms for automatic annotation/articulation of humans passing through a surveillance camera in a way useful for describing a person/criminal by a crime scene witness. Each human is articulated/annotated based on two appearance features: 1. primary colors of clothes in the head, body and legs region. 2. presence of text/logo on the clothes. The annotation occurs after a robust foreground extraction based on a modified approach to Gaussian Mixture model and detection of human from segmented foreground images. The proposed pipeline consists of a preprocessing stage where we improve color quality of images using a basic color constancy algorithm and further improve the results using a proposed post-processing method. The results show a significant improvement to the illumination of the video frames. In order to annotate color information for human clothes, we apply 3D Histogram analysis (with respect to Hue, Saturation and Value) on HSV converted image regions of human body parts along with extrema detection and thresholding to decide the dominant color of the region. In order to detect text/logo on the clothes as another feature to articulate humans, we begin with the extraction of connected components of enhanced horizontal, vertical and diagonal edges in the frames. These candidate regions are classified as text or non-text on the bases of their Local Energy based Shape Histogram (LESH) features combined with KL divergence as classification criteria. To detect humans, a novel technique has been proposed that uses a combination of Histogram of Oriented Gradients (HOG) and Contourlet transform based Local Binary Patterns (LBP) with Adaboost as classifier. Initial screening of foreground objects is performed by using HOG features. To further eliminate the false positives due to noise form background and improve results, we apply Contourlet-LBP feature extraction on the images. In the proposed method, we extract the LBP feature descriptor for Contourlet transformed high pass sub-images from vertical and diagonal directional bands. In the final stage, extracted Contourlet-LBP descriptors are applied to Adaboost for classification. The proposed frame work showed fairly fine performance when tested on a CCTV test dataset.

Existing deep learning techniques for radar-based fall event detection are computationally expensive and data hungry. This work proposes a novel methodology for radar-based fall event detection using computationally inexpensive traditional machine learning algorithms with the histogram of oriented gradients (HOG) of binary-encoded radar signatures. Radar signatures (binary) encoded using nonparametric local transforms, such as census transform (CT) and local binary pattern (LBP), contain enhanced gradient information and lead to distinguishable HOG features for better fall event detection. A comparative analysis of five different types of machine learning algorithms using HOG features from three different types of radar domain representations that are binary encoded in two different ways (CT and LBP) is presented. All evaluations are done on a publicly available dataset of human radar signatures. K-nearest neighbor algorithm using HOG features of binary-encoded spectrograms achieves an F1-score of 0.995.

To improve the performance of thyroid standard plane scanning, this paper proposes a classification algorithm for classifying thyroid ultrasound images using texture features. Firstly, the region of interest of ultrasound images is chosen in the preprocessing process step. Secondly, the local binary patterns (LBP) features and the histograms of oriented gradients (HOG) features of the images are extracted. Then the obtained feature vectors are spliced as the input of the support vector machine (SVM) which is used to classify the thyroid standard plane images. In the experiment, there are 4,574 thyroid standard plane images are used in this paper, which are divided into 8 categories, of which 3,655 pictures for training, and 919 pictures for testing. The experimental results show that the classification accuracy is up to 88.58%, which show a fact that the proposed algorithm has a good discriminating ability for the standard eight-category thyroid plane images, which can assist the doctor to diagnose to a certain extent.

Objectives: To present an automated computer vision-based traffic sign detection model created to identify roadside signs accurately in different environmental conditions. The goal is to improve detection accuracy using a lightweight architecture suitable for mobile devices. Methods: This study presents CV-TSD model. The technique integrates Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and TensorFlow-based machine learning for image recognition. Traffic sign datasets are collected from various online fora, annotated, and trained using convolutional neural networks. The proposed system uses Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and deep learning with TensorFlow for effective image classification. A custom dataset of traffic sign images was collected from several online sources, manually labelled, and used to retrain a convolutional neural network using transfer learning. Three environmental categories—dark shade, cloudy, and rainy—were used to test classification robustness. We assessed performance with training accuracy, validation accuracy, and cross-entropy loss. Findings: Experimental results show that CV-TSD consistently outperforms traditional HoG and LBP methods across all metrics. The system achieves a maximum training accuracy of 98% and a validation accuracy of 97% with a limited number of image samples. Furthermore, the model keeps stable cross-entropy values, indicating effective learning without overfitting. The lightweight design allows reliable performance even with smaller datasets, making it ideal for mobile and intelligent transportation systems. Novelty: Unlike standard traffic sign detection methods, CV-TSD mimics human visual perception by merging semantic image understanding with deep learning. Its ability to classify accurately with minimal data and low computational demands marks a significant advancement for real-time applications. Keywords: Traffic Sign Detection, Deep Learning, Image Classification, TensorFlow, HoG, LBP, Transfer Learning

This paper proposed a new method Based on curvature Based LBP feature to recognize 3D facial expression automatically. 3D facial expression images are described by means of four images which gray level are the value of curvature-Based descriptors (principal curvatures k1, k2, mean curvature, shape index) and then encoded by LBP. To efficiently optimize the performance, Chi-square distance is employed for classification. Finally, experimental result achieved on the Bosphorus database illustrates that the curvature-Based LBP (CLBP) has performs better than other features and also shows these features are significant for 3D facial expression recognition.

The face of man: Expressions of universal emotions in a New Guinea village

Facial emotion recognition using modified HOG and LBP features with deep stacked autoencoders

This paper presents the evaluation of visual features for the proposed two eye detection method applied to thermal images. The use of two eye region is due to its distinctive pattern and to overcome the issue of blurred and noisy characteristic in the thermal image. Comparative performance analysis on three different features which includes Haar, Histogram of Oriented Gradients (HoG) and Local Binary Patterns (LBP) is conducted. The performance of the eyes detection method is measured based on the correct detection of both eyes inside the face image. The experiments were done on the Natural Visible and Infrared Facial Expression Database (NVIE). The method proposed in this paper shows good eye detection accuracy. The best detection accuracy is obtained using the HoG feature.

In this paper a novel approach based on multiple color channels is proposed for multi-class fruit detection. Multi-feature fusion of global color histogram, Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG), and LBP based on Gabor wavelets (GaborLBP) is utilized to improve fruit recognition. These four features are extracted on multiple color channels. To select the optimal features of HOG, LBP and GaborLBP in each color channel, the optimal block is selected according to the cross validation accuracy. The optimal color channel fusion is optimized by a simple optimal color channel selection framework proposed in this paper. A multi-class fruit classifier is trained using the optimal color channel fusion and LibSVM. Besides, the proposed method is evaluated on a fruit dataset including 5 classes of fruit and 1778 test images. The experiments show that the proposed multi-class fruit detection method is effective. This proposed method has produced 0.156 miss rate at 0.5427 false positives per image (FPPI) and high average precision (0.8135), and can detect multi-class and multiple fruits in a variety of backgrounds, locations, angles, and sizes.

In today's world with the increase in pollution and other lifestyle changes of people, many individuals are prone to various cancer types. Out of these many cancer types Skin Cancer is the most common. Skin cancer is of two types i.e., benign and malignant. Timely detection of the skin cancer may lead to proper treatment and thus reduce the cost of treatment and hence reduces the damage to the skin-saving patients' life. For detecting skin cancer with high accuracy and very fast compared to the traditional methods this paper proposed the method of using texture-based features descriptors such as Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG). This research proposed the method of detecting skin cancer type from the RGB image of the target area of skin cancer. The proposed method used texture features of the image to detect skin cancer with help of LBP and HOG texture feature descriptors. Further Logistic Regression (LR) is applied for classifying whether skin cancer is benign or malignant. The results are very efficient as the accuracy achieved by utilizing LBP and HOG of RGB image data is 99.394%. A total of 3600 RGB Images have been used for such a study. The experimental analysis results obtained are compared with other techniques like Random Forest Classifier, Ada Boost Classifiers, and Logistic Regression and it is found that the proposed approach outperforms available algorithms.

Brain inspired face recognition: A computational framework

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.

This paper presents an empirical study for human behavior analysis based on three distinct feature extraction techniques: Histograms of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Scale Invariant Local Ternary Pattern (SILTP). The utilised public videos representing spatio-temporal problem area of investigation include INRIA person detection and Weizmann pedestrian activity datasets. For INRIA dataset, both LBP and HOG were able to eliminate redundant video data and show human-intelligible feature visualisation of extracted features required for classification tasks. However, for Weizmann dataset only HOG feature extraction was found to work well with classifying five selected activities/exercises (walking, running, skipping, jumping and jacking).

In order to overcome the problems of missing important information and di-mensionality disaster of three-dimensional (3D) facial expression, a novel method based on geometric feature fusion is proposed. This approach initially extracts distance feature vectors and angle feature vectors from key facial regions such as the eyes, mouth, and eyebrows using direct geometric feature. Subsequently, the K-nearest neighbor (K-NN) algorithm is used to obtain the distance feature vector candidate sets and angle feature vector candidate sets separately. Finally, the maximum-minimum rule is utilized to fuse these candidate sets into a single feature vector, completing the recognition of 3D facial expression. Experimental results on the BU-3DFE database demonstrate that this method achieves on overall recognition rate of 97.6%, exhibiting excellent robustness to varia-tions in facial expressions. Furthermore, this approach can serve as a valuable reference for future research in the field of 3D face recognition.