Pattern Recognition Research Research Articles

Evaluation of Japanese Teaching Quality Based on Deep Neural Network

The 21st century is an era of rapid development of information and frequent international exchanges, and Japanese language teaching has received increasing attention. Because of this, colleges and universities are now focused on improving the quality of Japanese education, both now and in the future. We need to boost the whole management of teaching quality, notably the assessment of instructors’ teaching quality, in order to improve teaching quality. However, because a number of factors influence the quality of instruction, and each factor’s weight varies, the evaluation results are difficult to express in a mathematical analytical formula, resulting in a complex nonlinear classification problem that traditional classification methods cannot solve well. As a new technology, as a result of the artificial neural networks (ANNs) fundamental qualities, it has been extensively applied in different evaluation issues for pattern recognition, nonlinear classification, and other research. This subject introduces the optimized deep neural network theory into Japanese teaching quality evaluation and completes the following work: (1) the algorithm of discrete Hopfield neural network is introduced in detail, and the neural network theory is introduced into teaching evaluation. (2) Then, based on the evaluation data of teachers’ teaching quality in a school, a large number of simulation experiments and training were carried out, and a neural network model for evaluation of teachers’ teaching effect was constructed and designed. Experiments reveal that the neural network model proposed in this paper is a nonlinear mapping method, which increases the evaluation’s dependability and makes the outcomes more effective and objective.

Development of Feature Extraction Technique

A problem of personal verification and identification is an actively growing area of research in pattern recognition, computer vision, and digital image processing. A Face acknowledgment system is largely significant move toward in our life. Face acknowledgment method must be proficient to robotically identify images of face. This system mainly used to recognize a person and make available a protection during various features of our life. It is extremely complicated job for investigator to obtain mainly excellent face acknowledgment velocity in a variety of circumstances and benchmarks. Face recognition is grassland of computer visualization to uses faces to recognize or authenticate a human being. Principal Component Analysis (PCA) is accomplished and used for feature extraction and measurement lessening. The feature extraction is used to reduce the dimension of the face space by transforming it into feature representation. Features may be symbolic, numerical or both. The symbolic feature is color and numerical feature is weight. The combined feature extraction of PCA, LDA and Wavelet are used in proposed feature extraction algorithm for human face recognition system. The structure is tested and achieves high recognition rates. Information regarding individuals was stored in a database.

Fusing Local and Global Information for One-Step Multi-View Subspace Clustering

Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.

Improved Alexnet-Based Character Detection Method

Extracting text information in complex images is a hot spot in pattern recognition research with broad application prospects. Natural scene door numbers produce serious distortion due to blurred images, uneven illumination and low light illumination, which makes it difficult to achieve ideal results in character recognition, and recognizing characters of arbitrary length is even more of a challenge. In this paper, we adopt the improved Niblack’s local threshold segmentation method to segment the images, and mark the connected areas of the segmented images to highlight the important features, and finally input the above pre-processed images to the improved AlexNet network for target detection on SVHN (Street View House Number) to realize the detection of characters in real scenes. The experimental results show that the improved Alexet-based target detection method is able to complete the detection task of streetscape door number characters well with 92.89% correct recognition rate.

Research and Implementation of Flow Pattern Recognition for Gas-liquid Two-phase Flows Based on GoogLeNet

In order to solve the flow pattern recognition problem of gas-liquid two-phase flow in pipelines, this paper uses high-speed photography to sample the flow patterns of transparent pipe sections and combines the GoogLeNet convolutional neural network model under migration learning to implement a flow pattern recognition method with small samples. In this paper, the GoogLeNet Inception V1 network is used, and the convolutional layer and the pooling layer weights parameters obtained from its training on the imageNet dataset are retained, and the flow pattern samples obtained on the gas-liquid two-phase flow experimental platform are used to train the network model. The recognition accuracy was 98.37% with a training set of 400 and a test set of 100 samples of each flow type. The convolutional neural network directly uses images as data input without operations such as image pre-processing and feature extraction, and its unique fine-grained feature extraction enables the recognition of images by convolutional neural networks at a nearly human level.

Research on Fitness Movement Monitoring System Based on Internet of Things.

A multiuser motion-monitoring system based on MEMS is proposed for fitness movement, it is used to monitor the three important parameters of movement type, movement times, and movement cycle in the body movement and supports the simultaneous use of multiple users. The specific content of the method: (1) In terms of system design, a motion-monitoring system framework based on the Internet of things is proposed considering the motion-monitoring scene oriented to intelligent fitness. (2) In the aspect of algorithm, the relevant research of motion pattern recognition and cycle calculation method is carried out. For action pattern recognition, SVM-based algorithm to adapt to different computing capabilities of the scene is applied. (3) Experiments on 7 kinds of actions show that the proposed deep neural network has a good learning effect on small datasets, the recognition accuracy of the proposed deep neural network reaches 97.61%, and the recognition accuracy of SVM also reaches over 96%. In the 50 times of operation cycle calculation experiments, the frequency statistics algorithm has reached 100% of the calculation accuracy, and the calculation results of the operation cycle are close to the real value, which proves the validity of the method of cycle calculation. The experiment proves that the zero-crossing detection and wavelet analysis methods have a good overall effect and can accurately count and calculate the period when the number of actions is more, improve fitness efficiency, and provide guarantee for human health.

Face Recognition Method based on Support Vector Machine and Rain Optimization Algorithm (ROA)

One basic study direction in pattern recognition research domain is Face recognition. Face recognition-based Authentication is used widely. Face recognition is related to non-linear issue; therefore, some techniques of artificial intelligence have been used in last few years to face recognition. According to recent results, support vector system classifiers (SVM) have excellent face recognition accuracy in pattern recognition in comparison with other classification methods. Although, support vector machine training parameters selection has great effect on the performance of support vector machine. Here in the research, the novel Rain optimization algorithm and support vector machine algorithm (ROA-SVM)-based method of face recognition is provided. In ROA-SVM, Rain optimization algorithm is applied for optimizing SVM parameters at the same time. The average classification accuracy for the YALE dataset in the proposed method was 86% and for the base paper was 81%. Furthermore, in order to find optimal parameters in support vector machine, proposed ROA-SVM method efficiency has been improved by 5 percent in comparison with PSO-SVM basic research.

Face Recognition under Illumination based on Optimized Neural Network

Face recognition is a significant area of pattern recognition and computer vision research. Illumination in face recognition is obvious yet challenging task in pattern matching. Recent researchers introduced machine learning algorithms to solve illumination problems in both indoor and outdoor scenarios. The major challenge in machine learning is the lack of classification accuracy. Thus, the novel Optimized Neural Network Algorithm (ONNA) is used to solve the aforementioned drawback. First, we propose a novel Weight Transfer Ideal Filter (WTIF) which is employed for pre-processing to remove the dark spots and shadows in an image by normalizing low frequency and high frequency of illumination. Secondly, Robust Principal Component Analysis (RPCA) is employed to perform efficient extraction of features based on image area representation. These features are given as input to ONNA which classifies the given input image under illumination. Thus we achieve the recognition of the face under various illumination conditions. Our approach is analyzed and compared with existing approaches such as Support Vector Machine (SVM) and Random Forest (RF). ONNA is better in terms of high accuracy and low error rate.

End-to-end Handwritten Chinese Paragraph Text Recognition Using Residual Attention Networks

Handwritten Chinese recognition which involves variant writing style, thousands of character categories and monotonous data mark process is a long-term focus in the field of pattern recognition research. The existing methods are facing huge challenges including the complex structure of character/line-touching, the discriminate ability of similar characters and the labeling of training datasets. To deal with these challenges, an end-to-end residual attention handwritten Chinese paragraph text recognition method is proposed, which uses fully convolutional neural networks as the main structure of feature extraction and employs connectionist temporal classification as a loss function. The novel residual attention gate block is more helpful in extracting essential features and making the training of deep convolutional neural networks more effective. In addition, we introduce the operations of batch bilinear interpolation which implement the mapping of two dimension text representation to one dimension text line representation without any position information of characters or text lines, and greatly reduce the labeling workload in preparing training datasets. In experimental, the proposed method is verified with two widely adopted handwritten Chinese text datasets, and achieves competitive results to the current state-of-the-art methods. Without using any position information of characters and text line, an accuracy rate of 90.53% is obtained in CASIA-HWDB test set.

Explicating ResNet for Facial Expression Recognition

Convolution Neural Network is one of the phenomenal formulations in the field of pattern recognition research, computer vision and image processing. It helped to facilitate many theories into real working models. One of them being Facial expression recognition (FER) which has been benefited with the development of CNN architectures, especially the ResNet architecture which has emerged as the winner of ImageNet 2015 competition. Residual Neural Network (ResNet) inculcates the idea of skip connections and became the most cited neural network. In this paper we have analyzed an extensive view of addressing facial expression and emotion recognition with the assistance of ResNet. We have particularly emphasized upon Resnet-18, Resnet-50, SobelResNet, ResNet with Attention mechanism and deformable convolution, Emotion recognition using heart rate variability analysis and ResNet, 3D inception ResNet layers.

An Innovative Approach for Automatic Character Recognition of Indian Languages

evelopment of OCRs for Indian script is an active area of activity today. Optical character recognition (OCR) is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text. In simple words OCR is a visual recognition process that turns printed or written text into an electronic character-based file. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques. A lot of work had been carried out for OCR at international scenario but in Indian context a concrete approach for character recognition is still required as scripts of Indian languages are from the group of most complex scripts and it is very hard to recognize them. Indian scripts present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. The problem is compounded by the unstructured manner in which popular fonts are designed. There is a lot of common structure in the different Indian scripts. All existing OCR systems developed for various Indian scripts do not provide sufficient efficiency due to various factors. The objective of this paper is to discuss a more efficient character recognition technique. This paper introduces a new technical approach to recognize Indian script characters which are unpredictable due to different problems in other OCR‟s.

Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing

Sound event detection, speech emotion classification, music classification, acoustic scene classification, audio tagging and several other audio pattern recognition applications are largely dependent on the growing machine learning technology. The audio pattern recognition issues are also addressed by neural networks in recent days. The existing systems operate within limited durations on specific datasets. Pretrained systems with large datasets in natural language processing and computer vision applications over the recent years perform well in several tasks. However, audio pattern recognition research with large-scale datasets is limited in the current scenario. In this paper, a large-scale audio dataset is used for training a pre-trained audio neural network. Several audio related tasks are performed by transferring this audio neural network. Several convolution neural networks are used for modeling the proposed audio neural network. The computational complexity and performance of this system are analyzed. The waveform and leg-mel spectrogram are used as input features in this architecture. During audio tagging, the proposed system outperforms the existing systems with a mean average of 0.45. The performance of the proposed model is demonstrated by applying the audio neural network to five specific audio pattern recognition tasks.

Sophisticated and modernized library running system with OCR algorithm using IoT

An internet of things (IoT) is an exclusive method, were its impact on the enactments of human life is very trendy. This research on library control system operates on the basis of IoT and optical character recognition (OCR) algorithm rules and its training. A closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as machine transformation, text to speech extraction and text data mining. It utilized in various area of research in artificial intelligence, computer vision and pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IoT based library management system to maintaining books in digital format.

Devanagari Handwritten Marathi Compound Character Recognition System

Handwritten character recognition a field of research in AI, computer vision, and pattern recognition. Devanagari handwritten Marathi compound character recognition is most tedious tasks because of its complexity as compared to other languages. As compound character is combination of two or more characters it becomes challenging task to recognize it. However, the researchers used various methods like Neural Network, SVM, KNN, Wavelet transformation to classify the features of compound Marathi characters and tried to give the accuracy in the recognition of it. But the problem of feature extraction, and time required is large. In this paper I am proposing the Offline handwritten Marathi compound character recognition using deep convolution neural network which reduces the computational time and increases the accuracy.

Research into vessel behaviour pattern recognition in the maritime domain: Past, present and future

A comprehensive literature review on vessel behaviour pattern recognition in maritime and littoral environments, spanning a period of three decades, is presented. A vast majority of research focuses on anomalous vessel behaviour detection from Automated Identification System (AIS) data, using artificial intelligence (AI), Bayesian networks and machine learning based methods. AIS is a cooperative system that employs VHF transceivers to share vessel information through terrestrial and satellite communication networks. To track small boats not fitted with AIS, uncooperative surveillance systems are employed. These systems have been studied in a relatively small number of publications. In vessel behaviour pattern recognition, target classification is critically important, particularly, in asymmetric warfare situations. The paper concludes with a discussion of future outlook in vessel behaviour pattern recognition research.

Human action recognition using attention based LSTM network with dilated CNN features

Human action recognition in videos is an active area of research in computer vision and pattern recognition. Nowadays, artificial intelligence (AI) based systems are needed for human-behavior assessment and security purposes. The existing action recognition techniques are mainly using pre-trained weights of different AI architectures for the visual representation of video frames in the training stage, which affect the features’ discrepancy determination, such as the distinction between the visual and temporal signs. To address this issue, we propose a bi-directional long short-term memory (BiLSTM) based attention mechanism with a dilated convolutional neural network (DCNN) that selectively focuses on effective features in the input frame to recognize the different human actions in the videos. In this diverse network, we use the DCNN layers to extract the salient discriminative features by using the residual blocks to upgrade the features that keep more information than a shallow layer. Furthermore, we feed these features into a BiLSTM to learn the long-term dependencies, which is followed by the attention mechanism to boost the performance and extract the additional high-level selective action related patterns and cues. We further use the center loss with Softmax to improve the loss function that achieves a higher performance in the video-based action classification. The proposed system is evaluated on three benchmarks, i.e., UCF11, UCF sports, and J-HMDB datasets for which it achieved a recognition rate of 98.3%, 99.1%, and 80.2%, respectively, showing 1%–3% improvement compared to the state-of-the-art (SOTA) methods.

Stretch Sensor-Based Facial Expression Recognition and Classification Using Machine Learning

Sensor-based Facial expression recognition (FER) is an attractive research topic. Nowadays, FER is used for different application such as smart environments and healthcare solutions. The machine can learn human emotion by using FER technology. It is the primary and essential for quantitative analysis of human sentiments. FER is an image recognition problem within the broader field of computer vision. Face detection and tracking, reliable face recognition still present a considerable challenge for researchers in computer vision and pattern recognition. First, data processing and analytics are intensive and require a large number of computation resources and memory. Second, the fundamental technical limitation is its robustness in changes in the environment. Finally, illumination variation further complicates the design of robust algorithms because of changes in shadow casts. However, sensor-based FER overcomes all these limitations. Sensor technologies, especially low-power, wireless communication, high-capacity, and data processing have made substantial progress, making it possible for sensors to evolve from low-level data collection and transmission to high-level inference. This study aims to develop a stretchable sensor-based FER system. We use random forest machine learning algorithms used for training the FER model. Commercial stretchable facial expression dataset is simulated into the anaconda software. In this research, our stretch sensor FER dataset obtained around 95% accuracy for four different emotions (Neutral, Happy, Sad, and Disgust).

Multiscale Control Chart Pattern Recognition Using Histogram-Based Representation of Value and Zero-Crossing Rate

In the research of control chart pattern recognition (CCPR), most previous methods used a classifier to label abnormal CCPs. However, long-term control chart data often contains a large number of small abnormal patterns, with characteristics unlike those seen from a global view of the entire chart. There is also a high probability that local abnormal patterns are worthy of analysis. This article presents a novel multiscale control chart pattern recognition scheme, MS-CCPR, which does not focus on the classification of data from a single chart. Rather, the proposed scheme uses a proposed histogram-based data representation in conjunction with time series subsequence matching to identify abnormal patterns on various scales from a long series of control charts. Experimental results demonstrate the efficacy of the proposed framework in the efficient detection of chart patterns at various scales, outperforming the state-of-the-art time series subsequence matching algorithms.

Research of Network Behavior Pattern Recognition Based on Power Monitoring System

Based on the network security protection of the power monitoring system, this paper proposes a pattern recognition model for the operation behavior of the operation and maintenance personnel of power monitoring system. Through the technical research of the alarm pattern recognition of the network security management platform, and carries out the operation type data of the network security management platform Clustering analysis uses historical data to intelligently train the clustering model. Through training, the risk level classification of the operation behavior is obtained, and then real-time data is introduced into the model for detection, which can judge the degree of risk of the operation event in real time. With the improvement of the K-means clustering algorithm and the application of big data analysis, it further improves the he intelligence level of the platform and provides technical support for the intelligent protection of network.

Facial Expression Recognition Using Dynamic Local Ternary Patterns With Kernel Extreme Learning Machine Classifier

Rapid growth in advanced human-computer interaction (HCI) based applications has led to the immense popularity of facial expression recognition (FER) research among computer vision and pattern recognition researchers. Lately, a robust texture descriptor named Dynamic Local Ternary Pattern (DLTP) developed for face liveness detection has proved to be very useful in preserving facial texture information. The findings motivated us to investigate DLTP in more detail and examine its usefulness in the FER task. To this end, a FER pipeline is developed, which uses a sequence of steps to detect possible facial expressions in a given input image. Given an input image, the pipeline first locates and registers faces in it. In the next step, using an image enhancement operator, the FER pipeline enhances the facial images. Afterward, from the enhanced images, facial features are extracted using the DLTP descriptor. Subsequently, the pipeline reduces dimensions of the high-dimensional DLTP features via Principal Component Analysis (PCA). Finally, using the multi-class Kernel Extreme Learning Machine (K-ELM) classifier, the proposed FER scheme classifies the features into facial expressions. Extensive experiments performed on four in-the-lab and one in-the-wild FER datasets confirmed the superiority of the method. Besides, the cross-dataset experiments performed on different combinations of the FER datasets revealed its robustness. Comparison results with several state-of-the-art FER methods demonstrate the usefulness of the proposed FER scheme. The pipeline with a recognition accuracy of 99.76%, 99.72%, 93.98%, 96.71%, and 78.75%, respectively, on the CK+, RaF, KDEF, JAFFE, and RAF-DB datasets, outperformed the previous state-of-the-art.