The new proposed method for texture modification of closed up face image based on image processing using local weighting pattern (LWP) with enhancement technique

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. This article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. Data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.KeywordsDeep neural network algorithmComputer visionPattern recognitionTechnical application

In this paper, we describe a novel local descriptor of image texture representation for visual recognition. The image features based on micro-descriptors such as local binary patterns (LBP) and local ternary patterns (LTP) have been very successful in a number of applications including face recognition, object detection, and texture analysis. Instead of binary quantization in LBP, LTP thresholds the differential values between a focused pixel and its neighborhood pixels into three gray levels, which can be explained as the active status (i.e., positively activated, negatively activated, and not activated) of the neighborhood pixels compared to the focused pixel. However, regardless of the magnitude of the focused pixel, the thresholding strategy remains fixed, which would violate the principle of human perception. Therefore, in this study, we design LTP with a data-driven threshold according to Weber’s law, a human perception principle; further, our approach incorporates the contexts of spatial and orientation co-occurrences (i.e., co-occurrence context) among adjacent Weber-based local ternary patterns (WLTPs, i.e., data-driven quantized LTPs) for texture representation. The explored WLTP is formulated by adaptively quantizing differential values between neighborhood pixels and the focused pixel as negative or positive stimuli if the normalized differential values are large; otherwise, the stimulus is set to 0. Our approach here is based on the fact that human perception of a distinguished pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. By integrating co-occurrence context information, we further propose a rotation invariant co-occurrence WLTP (RICWLTP) approach to be more discriminant for image representation. In order to validate the efficiency of our proposed strategy, we apply this to three different visual recognition applications including two texture datasets and one food image dataset and prove the promising performance that can be achieved compared with the state-of-the-art approaches.

A rapid growth in medical ultrasound database makes it difficult for medical practitioners to manage and search relevant data with good efficiency. Hence, a novel image retrieval technique using Mean Distance Local Binary Pattern (Mean Distance LBP) has been proposed for content-based image retrieval. The conventional local binary pattern (LBP) converts every pixel of image into a binary pattern based on their relationship with neighbourhood pixels. The proposed feature descriptor differs from local binary pattern as it transforms the mutual relationship of all neighbouring pixels in a binary pattern based on their standard deviation templates as well as Euclidean distance from the center pixel. Color feature and Gray Level Co-occurrence Matrix have also been used in this work. To prove the excellence of the proposed method, experiments have been conducted on two different databases of natural images and face images. Further, the method is applied on real time ultrasound database for retrieval of liver images from a set of ultrasound images of various organs. The performance has been observed using well-known evaluation measures, precision and recall, and compared with some state-of-art local patterns. Comparison shows a significant improvement in the proposed method over existing methods.

Because of its wide application value, behavior recognition has long been one of the research hots pots in the field of computer vision and pattern recognition. At present, the method based on local features and word packet model has been widely used in the field of behavior recognition. However, this method does not consider the temporal and spatial relationship between features, and the local temporal and spatial relationship between features is very important for behavior representation and behavior recognition. In view of the above problems, this paper proposes a modeling method of character behavior recognition based on local spatio-temporal relationship in surveillance video. Firstly, each part of the proposed network model is introduced in detail, and then the proposed model is compared with the advanced skeleton action recognition methods in recent years on several skeleton data sets. Finally, the effectiveness of the proposed method is verified. The experimental results show that, compared with the recognition results of related literatures, the features extracted by choosing the starting point of trajectory have better recognition performance under the fusion framework.

Content-based image retrieval is an important application area for image processing methods associated with computer vision, pattern recognition, machine learning and other fields of artificial intelligence. Image content analysis enables us to use more natural, human-level concepts for querying large collections of images typically found in multimedia databases. Out of the numerous features proposed for image content description those based on frequency representation are of special interest as they often offer high levels of invariance to distortions and noise. In this chapter several frequency domain methods designed to describe different aspects of an image, i.e. contour, texture and shape are discussed. Current standards and database solutions supporting content-based image retrieval, including SQL Multimedia and Application Packages, Oracle 9i/10g interMedia and MPEG-7, are also presented.

—Support Vector Machine (SVM), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern Histogram (LBPH) are some of the additional methods that CNN takes into consideration when calculating the greatest accuracy. The Convolution Neural Network (CNN) is a model that suggests the classification of the input image of the animal. We are in the process of developing a database of wild animals; our database system is comprised of pictures of each category. The results of this experiment demonstrate that overall results were produced in order to check the impact that various processing images have on the beneficial impact that their output has on other processes. Deep Convolutional Neural Networks, often known as DCNNs, are a way of learning picture features that is both efficient and selective. This technology has been extensively researched and widely used in the field of computer vision and pattern recognition. An investigation into the application of machine learning strategies to animal photographs is presented in this work. The purpose of this research is to improve the accuracy of scene classification.

Over the past several years, the demand for eye tracking is increasing across fields of computer vision and pattern recognition, especially in commercial applications. However, the low prediction accuracy and the restriction of datasets and methods for special eye tracking equipment have been obstacles of the wide application of gaze estimation. In this paper, we develop an Android-based acquisition software named EyeTracker, to collect the first Chinese gaze dataset. And then we proposed a convolutional neural network framework for gaze estimation in eye tracking based on a single image. We evaluate our proposed analysis model on our dataset-EyeTrackD (tablet) and Gazecapture (part of phone data). Our model achieves a prediction error of 4.33 cm and 2.25 cm on these two datasets respectively, which are better than the previous method using the same data. Extensive experiments under different network settings show the effectiveness of our convolutional neural network framework.

Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as a meaningful physical movement of the fingers, hands, arms, or other parts of the body with the purpose to convey information for the environment interaction. For instance, hand gesture recognition (HGR) can be used to recognize sign language which is the primary means of communication by the deaf and mute. Vision-based HGR is critical in its application; however, there are challenges that will need to be overcome such as variations in the background, illuminations, hand orientation and size and similarities among gestures. The traditional machine learning approach has been widely used in vision-based HGR in recent years but the complexity of its processing has been a major challenge—especially on the handcrafted feature extraction. The effectiveness of the handcrafted feature extraction technique was not proven across various datasets in comparison to deep learning techniques. Therefore, a hybrid network architecture dubbed as Lightweight VGG16 and Random Forest (Lightweight VGG16-RF) is proposed for vision-based hand gesture recognition. The proposed model adopts feature extraction techniques via the convolutional neural network (CNN) while using the machine learning method to perform classification. Experiments were carried out on publicly available datasets such as American Sign Language (ASL), ASL Digits and NUS Hand Posture dataset. The experimental results demonstrate that the proposed model, a combination of lightweight VGG16 and random forest, outperforms other methods.

As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

Sign language recognition is an emerging field of computer vision and pattern recognition that enables deaf individuals to communicate with hearing people. With deep learning and advancements in computer vision, computers can automatically recognize hand shapes and motion dynamics, facial expressions, and body postures in sign language. Even though deep neural networks (DNNs) perform well on vision tasks, they consume excessive computational resources and have a large memory footprint. Thus, DNNs cannot be deployed on edge devices. Quantization is one of the most useful model compression techniques for overcoming the limitations of deploying these large models on devices with limited computing resources. This paper proposes an improved post-training quantization method, called the hybrid percentile-based outlier aware dynamic range quantization approach, which sets clipping thresholds layerwise to minimize quantization error. This hybrid quantization approach maintains FP32 precision for input weights, the final output classification layer, and activations, while quantizing only the weights in intermediate layers to achieve computational efficiency. Itworks well in layers with heavy-tailed weight distribution. Experimentation is conducted using benchmark deep learning models, including VGG16, ResNet50, EfficientNetB0, and SqueezeNet. The experiments use a widely used standard ASL Alphabet dataset. The proposed method is robust to outliers and achieves up to 10−11× reductions in model size and 11−122× inference speedups, with less than 1% accuracy drop relative to baseline models with 32-bit floating-point precisions. The findings show that the proposedhybrid quantization method accelerates the inference speed of deep learning models while maintaining optimal performance.

Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called the Feature-Based Structural Measure (FSM), combines the best features of the well-known SSIM (structural similarity index measure) and FSIM (feature similarity index measure) approaches, striking a balance between performance for similar and dissimilar images of human faces. In addition to the statistical structural properties provided by SSIM, edge detection is incorporated in FSM as a distinctive structural feature. Its performance is tested for a wide range of PSNR (peak signal-to-noise ratio), using ORL (Olivetti Research Laboratory, now AT&T Laboratory Cambridge) and FEI (Faculty of Industrial Engineering, São Bernardo do Campo, São Paulo, Brazil) databases. The proposed measure is tested under conditions of Gaussian noise; simulation results show that the proposed FSM outperforms the well-known SSIM and FSIM approaches in its efficiency of similarity detection and recognition of human faces.

Face recognition has attracted a lot of interest in the fields of computer vision and pattern recognition given its extensive applications in security, surveillance, and human-computer interaction. Many linear and non-linear classifiers have been introduced to bring about effectiveness in face recognition, however, the problem of occlusion, light conditions and changes in face persist. The Heskes-self-organizing map (SOM) counter propagation network (CPN) model leverages the competitive learning and self-organizing features of SOM CPN with Heskes layer to improve the effectiveness and accuracy of face recognition systems. Heskes-SOM CPN was implemented and evaluated on MATLAB R2016a using 600 images captured with the aim of digital camera. The implemented model was trained with 360 face images and tested with 240 face images using accuracy, sensitivity, specificity, and false positive rate as performance metrics at four distinct threshold values of 0.23, 0.35, 0.50, and 0.75. The major objective of the research was achieved by investigating with 50×50 and 200×200 face dimensions. Empirical results and statistical evidence established that Heskes-SOM CPN has high accuracy of approximately 97.92%, high specificity of 98.33%, high sensitivity of 99.44%, and a very low filter performance rating (FPR) of 1.67%. Therefore, Heskes-SOM CPN is presented as a novel CPN model for face recognition.

Robust feature matching using guided local outlier factor

Face recognition has become a research hotspot in the fields of computer vision, pattern recognition, and machine learning. The accuracy of recognizing faces with varying expressions and illumination as well as occlusions and noise, on the other hand, presents a unique challenge in face recognition systems. Facial images must be preprocessed for improved recognition accuracy. A major issue with the existing approaches is that they have limited capacity that cannot handle large-scale occlusion and noise situations adequately. In this paper, we present Low -rank matrix approximation (LRMA) models like Robust principal component analysis (RPCA), Weighted Nuclear Norm Minimization (WNNM), and Weighted Schatten p-norm (WSNM) for Robust face recognition. A confusion matrix is used for calculating the accuracy of face recognition. Experiments are conducted and the performance of these LRMA models is compared using the Yale database with facial occlusions, poor illumination, expressions, and noise. The results show, both intuitively and numerically, that WSNM outperforms RPCA in removing facial occlusions, resulting in restored low-rank images with greater PSNR and SSIM.

Minimum Description Length (MDL) estimation has proven itself of major importance in a large number of applications many of which are in the fields of computer vision and pattern recognition. A problem is encountered in applying the associated formulas, however, especially those associated with model cost. This is because most of these are asymptotic forms appropriate only for large sample sizes. J. Rissanen has recently derived sharper code-length formulas valid for much smaller sample sizes. Because of the importance of these results, it is our intent here to present a tutorial description of them. In keeping with this goal we have chosen a simple application whose relative tractability allows it to be explored more deeply than most problems: the segmentation of binary strings based on a piecewise Bernoulli assumption. By that we mean that the strings are assumed to be divided into substrings, the bits of which are assumed to have been generated by a single (within a substring) Bernoulli source.