A Multi Model Framework for Grading of Human Emotion Using CNN and Computer Vision

Emotion analysis is an area which is been widely used in the forensic crime detection domain, a mentoring device for depressed students, psychologically affected patient treatment. The current system helps only in identifying the emotions but not in identifying the level of emotions like whether the individual is truly happy/sad or pretending to be happy /sad. In this proposed work a novel methodology has been introduced. We have rebuilt the Traditional Local Binary Pattern (LBP) feature operator to image the expression and combine the abstract characteristics of facial expression learned from the neural network of deep convolution with the modified features of the texture of the LBP facial expression in the full connection layer. These extracted features have been subjected as input for CNN Alex Net to classify the level of emotions. The results obtained in this phase are used in the confusion matrix for analysis of grading of emotions like Grade-1, Grade-2, and Grade-3 obtained an accuracy of 87.58% in the comparative analysis.

A Deep Learning based approach to classification of Baby Sign Language images

Baby Sign Language is used by hearing parents to hearing infants as a preverbal communication which reduce frustration of parents and accelerated learning in babies, increases parent-child bonding, and lets babies communicate vital information, such as if they are hurt or hungry is known as a Baby Sign Language . In the current research work, a study of various existing sign language has been carried out as literature and then after realizing that there is no dataset available for Baby Sign Language, we have created a static dataset for 311 baby signs, which were classified using a MobileNet V1, pretrained Convolution Neural Network [CNN].The focus of the paper is to analyze the effect of Gradient Descent based optimizers, Adam and its variants, Rmsprop optimizers on fine-tuned pretrained CNN model MobileNet V1 that has been trained using customized dataset. The optimizers are used to train and test on MobileNet for 100 epochs on the dataset created for 311 baby Signs. These 10 optimizers Adadelta, Adam, Adamax, SGD, Adagrad, RMSProp were compared based on their processing time.

SVM-based switching filter hardware design for mixed noise reduction in digital images using high-level synthesis tools

Impulse and Gaussian are the two most common types of noise that affect digital images due to imperfections in the imaging process, compression, storage and communication. The conventional filtering approaches, however, reduce the image quality in terms of sharpness and resolution while suppressing the effects of noise. In this work, a machine learning-based filtering structure has been proposed preserves the image quality while effectively removing the noise. Specifically, a support vector machine classifier is employed to detect the type of noise affecting each pixel to select an appropriate filter. The choice of filters includes Median and Bilateral filters of different kernel sizes. The classifier is trained using example images with known noise parameters. The proposed filtering structure has been shown to perform better than the conventional approaches in terms of image quality metrics. Moreover, the design has been implemented as a hardware accelerator on an FPGA device using high-level synthesis tools.

COVID-CLNet; COVID-19 Detection with Compressive Deep Learning Approaches

One of the most serious global health threats is COVID-19 pandemic. The emphasis on increasing the diagnostic capability helps stopping its spread significantly. Therefore, to assist the radiologist or other medical professional to detect and identify the COVID-19 cases in the shortest possible time, we propose a computer-aided detection (CADe) system that uses the computed tomography (CT) scan images. This proposed boosted deep learning network (CLNet) is based on the implementation of Deep Learning (DL) networks as a complementary to the Compressive Learning (CL). We utilize our inception feature extraction technique in the measurement domain using CL to represent the data features into a new space with less dimensionality before accessing the Convolutional Neural Network. All original features have been contributed equally to the new space using a sensing matrix. Experiments performed on different compressed methods show promising results for COVID-19 detection.

A State of the art Survey on Face Recognition Methods

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

A Texture Features based Robust Facial Expression Recognition

Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.

Classification of Autistic Spectrum Disorder Using Deep Neural Network With Particle Swarm Optimization

In this paper, Feature Selection Technique (FST) namely Particle Swarm Optimization (PSO) has been used. The filter based PSO is a search method with Correlation-based Feature Selection (CBFS) as a fitness function. The FST has two key goals of improving classification efficiency and reducing feature counts. Artificial Neural Network (ANN) Based Multilayer Perceptron Network (MLP) and Deep Learning (DL) have been considered the classification methods on 2 benchmark Autistic Spectrum Disorder (ASD) dataset. The experimental result was compared to the non-reduced features and reduced feature of ASD datasets. The reduced feature give up enhanced results in both classifiers MLP and DL. In addition, an experimental study on the exhibitions of these methodologies has been conducted. Finally, a new trend of PSO-MLP and PSO-DL based classification model is proposed.

Visual Tracking with Object Center Displacement and CenterNet

Modern artificial intelligence systems have revolutionized approaches to scientific and technological challenges in a variety of fields, thus remarkable improvements in the quality of state-of-the-art computer vision and other techniques are observed; object tracking in video frames is a vital field of research that provides information about objects and their trajectories. This paper presents an object tracking method basing on optical flow generated between frames and a ConvNet method. Initially, optical center displacement is employed to detect possible the bounding box center of the tracked object. Then, CenterNet is used for object position correction. Given the initial set of points (i.e., bounding box) in first frame, the tracker tries to follow the motion of center of these points by looking at its direction of change in calculated optical flow with next frame, a correction mechanism takes place and waits for motions that surpass a correction threshold to launch position corrections.

Screening of Radiological Images Suspected of Containing Lung Nodules

This paper presents a study about screening large radiological image streams produced in hospitals for earlier detection of lung nodules. Being one of the most difficult classification tasks in the literature, our objective is to measure how well state-of-the-art classifiers can screen out the images stream to keep as many positive cases as possible in an output stream to be inspected by clinicians. We performed several experiments with different image resolutions and training datasets from different sources, always taking ResNet-152 as the base neural network. Results over existing datasets show that, contrary to other diseases like pneumonia, detecting nodules is a hard task when using only radiographies. Indeed, final diagnosis by clinicians is usually performed with much more precise images like computed tomographies.

Bandwidth Estimation and Optimized Bitrate Selection for Dynamic Adaptive Streaming Over Http Using RSI-GM And ISSO

Dynamic Adaptive Streaming over HTTP (DASH) is an emerging solution that aims to standardize existing proprietary streaming systems. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. In this paper, a novel Relative Strength Index (RSI) with Geometric mean (GM) namely RSI-GM is proposed for estimating available bandwidth for DASH. The proposed work starts by taking the video as an input at the transmitter side and then the video compression is performed using the TRLE. Then MD5 hashing-based AES encryption is applied to the compressed video data to provide data security. Then RSI-GM is proposed to estimate the available bandwidth for DASH. Finally, after estimation, the bitrate for estimated bandwidth is selected optimally using the Improved Shark Smell Optimization (ISSO) algorithm.