Palm Print Identification Using Improved Histogram of Oriented Lines

Automatic palmprint identification has received much attention in security applications and law enforcement. The performance of a palmprint identification system is improved by means of feature extraction and classification. Feature extraction methods such as Subspace learning are highly sensitive to the rotation variances, translation and illumination in image identification. Thus, Histogram of Oriented Lines (HOL) has not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named Improved Histogram of Oriented Lines (IHOL), which is an alternative of HOL. Improved HOL is not very sensitive to changes of translation and illumination, and has the robustness against small transformations whereas the small translation and rotations make no change in histogram value adjustment of the proposed work. The experiment results show that based on IHOL, with Principal Component Analysis (PCA) subspace learning can achieve high recognition rates. The proposed method (IHOL-Cosine distance) improves 1.30% on PolyU I database, and similarly (IHOL-Euclidean distance) improves 2.36% on COEP database compared with existing HOL method.

Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects

This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws,nuts,keys,and coins:the histogram of oriented gradients(HOG)and local binary pattern(LBP).The desired features for the labeled images are first extracted and saved in the form of a feature matrix.Using three different classification methods(non-parametric K-nearest neighbors algorithm,support vector machine,and naïve Bayesian method),the images are classified into four different classes.Then,by examining the resulting confusion matrix,the performances of the HOG and LBP approaches are compared for these four classes.The effectiveness of these two methods is also compared with the“You Only Look Once”and faster region-based convolutional neural network approaches,which are based on deep learning.The collected image set in this paper includes 800 labeled training images and 180 test images.The results show that the use of the HOG is more efficient than the use of the LBP.Moreover,a combination of the HOG and LBP provides better results than either alone.

HSPOG:An Optimized Target Recognition Method Based on Histogram of Spatial Pyramid Oriented Gradients

The Histograms of Oriented Gradients(HOG)can produce good results in an image target recognition mission,but it requires the same size of the target images for classification of inputs.In response to this shortcoming,this paper performs spatial pyramid segmentation on target images of any size,gets the pixel size of each image block dynamically,and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer.The new feature is called the Histogram of Spatial Pyramid Oriented Gradients(HSPOG).This approach can obtain stable vectors for images of any size,and increase the target detection rate in the image recognition process significantly.Finally,the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.

Ship detection and extraction using visual saliency and histogram of oriented gradient

A novel unsupervised ship detection and extraction method is proposed. A combination model based on visual saliency is constructed for searching the ship target regions and suppressing the false alarms. The salient target regions are extracted and marked through segmentation. Radon transform is applied to confirm the suspected ship targets with symmetry profiles. Then, a new descriptor, improved histogram of oriented gradient(HOG), is introduced to discriminate the real ships. The experimental results on real optical remote sensing images demonstrate that plenty of ships can be extracted and located successfully, and the number of ships can be accurately acquired. Furthermore, the proposed method is superior to the contrastive methods in terms of both accuracy rate and false alarm rate.

Pashto Characters Recognition Using Multi-Class Enabled Support Vector Machine

During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research community.The slight change in the Pashto characters’shape is an additional challenge for researchers.This paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques.These feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature maps.This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language.Recognition results are generated for the proposed model based on a manifold and hybrid feature map.An overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps respectively.Applicability of the proposed model is also tested by comparing its results with a convolution neural network model.The convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector machine.The highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.

Facial expression recognition using threestage support vector machines

Herein,a three-stage support vector machine(SVM)for facial expression recognition is proposed.The first stage comprises 21 SVMs,which are all the binary combinations of seven expressions.If one expression is dominant,then the first stage will suffice;if two are dominant,then the second stage is used;and,if three are dominant,the third stage is used.These multilevel stages help reduce the possibility of experiencing an error as much as possible.Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage.Facial expressions are created as a result of muscle movements on the face.These subtle movements are detected by the histogram-oriented gradient feature,because it is sensitive to the shapes of objects.The features attained are then used to train the three-stage SVM.Two different validation methods were used:the leave-one-out and K-fold tests.Experimental results on three databases(Japanese Female Facial Expression,Extended Cohn-Kanade Dataset,and Radboud Faces Database)show that the proposed system is competitive and has better performance compared with other works.

Human Action Recognition Based on Dense Trajectories Analysis and Random Forest

This paper presents a human action recognition method. It analyzes the spatio-temporal grids along the dense trajectories and generates the histogram of oriented gradients(HOG) and histogram of optical flow(HOF) to describe the appearance and motion of the human object. Then,HOG combined with HOF is converted to bag-of-words(Bo Ws) by the vocabulary tree. Finally,it applies random forest to recognize the type of human action. In the experiments,KTH database and URADL database are tested for the performance evaluation. Comparing with the other approaches,we show that our approach has a better performance for the action videos with high inter-class and low inter-class variabilities. Index TermsBag-of-words(Bo Ws),dense trajectories,histogram of optical flow(HOF),histogram of oriented gradient(HOG),random forest,vocabulary tree.

Traffic Flow Statistics Method Based on Deep Learning and Multi-Feature Fusion

Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algorithm,the Kalman filter(KF),which is only suitable for linear problems,is replaced by the extended Kalman filter(EKF),which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient(HOG)of the target.The multi-target tracking framework was constructed with YOLO V5 target detection algorithm.An efficient and longrunning Traffic Flow Statistical framework(TFSF)is established based on the tracking framework.Virtual lines are set up to record the movement direction of vehicles to more accurate and detailed statistics of traffic flow.In order to verify the robustness and accuracy of the traffic flow statistical framework,the traffic flow in different scenes of actual road conditions was collected for verification.The experimental validation shows that the accuracy of the traffic statistics framework reaches more than 93%,and the running speed under the detection data set in this paper is 32.7FPS,which can meet the real-time requirements and has a particular significance for the development of intelligent transportation.

Robust Interactive Method for Hand Gestures Recognition Using Machine Learning

The Hand Gestures Recognition(HGR)System can be employed to facilitate communication between humans and computers instead of using special input and output devices.These devices may complicate communication with computers especially for people with disabilities.Hand gestures can be defined as a natural human-to-human communication method,which also can be used in human-computer interaction.Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy.Thiswork aims to develop a powerful hand gesture recognition model with a 100%recognition rate.We proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve accuracy.The majority voting method was used to aggregate accuracies produced by each classifier and get the final classification result.Our model was trained using a self-constructed dataset containing 1600 images of ten different hand gestures.The employing of canny’s edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition rate.The experimental results had shown the robustness of our proposed model.Logistic Regression and Support Vector Machine have achieved 100%accuracy.The developed model was validated using two public datasets,and the findings have proved that our model outperformed other compared studies.

Detection of engineering vehicles in high-resolution monitoring images

This paper presents a novel formulation for detecting objects with articulated rigid bodies from highresolution monitoring images, particularly engineering vehicles. There are many pixels in high-resolution monitoring images, and most of them represent the background. Our method first detects ob ject patches from monitoring images using a coarse detection process. In this phase, we build a descriptor based on histograms of oriented gradient, which contain color frequency information. Then we use a linear support vector machine to rapidly detect many image patches that may contain ob ject parts, with a low false negative rate and a high false positive rate. In the second phase, we apply a refinement classification to determine the patches that actually contain ob jects. In this stage, we increase the size of the image patches so that they include the complete ob ject using models of the ob ject parts.Then an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. The detection process returns the absolute position of positive ob jects in the original images. We have applied our methods to three datasets to demonstrate their effectiveness.

Periocular Biometric Recognition for Masked Faces

Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.