Communication without words

New research into human-computer interaction seeks to consider the consumer's emotional status to provide a seamless human-computer interface. This would make it possible for people to survive and be used in widespread fields, including education and medicine. Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies. This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies. Multimodal affective computing systems are studied alongside unimodal solutions as they offer higher accuracy of classification. Accuracy varies according to the number of emotions observed, features extracted, classification system and database consistency. Numerous theories on the methodology of emotional detection and recent emotional science address the following topics. This would encourage studies to understand better physiological signals of the current state of the science and its emotional awareness problems.

/pdf/multimodal-emotion-recognition-using-deep-learning-2a8rycahpq.pdf

Multimodal Emotion Recognition using Deep Learning

Human face recognition have been an active research area for the last few decades. Especially, during the last five years, it has gained significant research attention from multiple domains like computer vision, machine learning and artificial intelligence due to its remarkable progress and broad social applications. The primary goal of any face recognition system is to recognize the human identity from the static images, video data, data-streams and the knowledge of the context in which these data components are being actively used. In this review, we have highlighted major applications, challenges and trends of face recognition systems in social and scientific domains. The prime objective of this research is to sum-up recent face recognition techniques and develop a broad understanding of how these techniques behave on different datasets. Moreover, we discuss some key challenges such as variability in illumination, pose, aging, cosmetics, scale, occlusion, and background. Along with classical face recognition techniques, most recent research directions are deeply investigated, i.e., deep learning, sparse models and fuzzy set theory. Additionally, basic methodologies are briefly discussed, while contemporary research contributions are examined in broader details. Finally, this research presents future aspects of face recognition technologies and its potential significance in the upcoming digital society.

Classical and modern face recognition approaches: a complete review

A survey on facial emotion recognition techniques: A state-of-the-art literature review

Over the past decades, facial expression recognition (FER) has become an interesting research area and achieved substantial progress in computer vision. FER is to detect human emotional state related to biometric traits. Developing a machine based human FER system is a quite challenging task. Various FER systems are developed by analysing facial muscle motion and skin deformation based algorithms. In conventional FER system, the developed algorithms work on the constrained database. In the unconstrained environment, the efficacy of existing algorithms is limited due to certain issues during image acquisition. This study presents a detailed study on FER techniques, classifiers and datasets used for analysing the efficacy of the recognition techniques. Moreover, this survey will assist researchers in understanding the strategies and innovative methods that address the issues in a real-time application. Finally, the review presents the challenges encountered by FER system along with the future direction.

Facial expression recognition techniques: a comprehensive survey

Deep learning has received increasing attention in all fields and has made considerable progress in facial expression recognition (FER). Mainly, the conventional FER methods are trained for constrained datasets, which may not operate well for real-time images. Such real-time image sequences limit the accuracy and efficacy of the traditional system. In this work, the authors present a novel deep learning framework which combines convolutional neural network (CNN) with long short-term memory (LSTM) cell for real-time FER. The novel framework has three main aspects: (i) two different pre-processing techniques are employed to handle illumination variances and to preserve subtle edge information of each image; (ii) correspondingly, the pre-processed images are inputted to the two individual CNN architecture which extracts the spatial features very effectively; (iii) spatial feature maps from two individual CNN layers are fused and integrated with an LSTM layer which extracts temporal relations between the successive frames. They experimented the authors’ proposed method on three publically available FER databases and also with self-created database. With pre-processing, their proposed model achieves comparable and better results to the state-of-the-art.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.1188

Novel deep learning model for facial expression recognition based on maximum boosted CNN and LSTM

Mathematical approach for segmenting chromosome clusters in metaspread images.

Chromosomes display a banding pattern when they are treated with some stains. Bands are simply alternating light and dark stripes that appear along the lengths of chromosomes. Banding pattern is one of the unique parameters in karyotyping chromosomes to detect chromosomal abnormalities. These banding patterns are used to identify the type of chromosomes and to diagnose chromosomal aberrations, including chromosome breakage, duplication, loss, translocation or inverted segments. There are four different segmentation algorithm is used for efficient detection of chromosome bands. These segmentation algorithms directly work on the intensity value. Firstly, watershed algorithm using distance transform is applied to the chromosome for segmentation. Over segmentation due to watershed segmentation is overcome by marker controlled watershed segmentation. Bands are not identified correctly for all the tested chromosomes using marker controlled watershed segmentation. Multi thresholding is applied where only few bands are identified. The segmentation algorithm based on watershed transform and graph cut method proves itself efficient and much better for the separation of bands.

Graph Partitioning approach for Segmentation of Banding Pattern of G-band Metaphase Human Chromosomes

Welding is the most significant component in day-to-day life. Without welding, nothing is possible which relates aircraft to skyrocket, the pipeline to highways, cars to workplace constructions. The major problem related to welding is the occurrence of crack at high temperatures during solidification. Sulphide stress corrosion cracking is a major problem to be analysed during pipeline girth welds. The crack occurs when the test stress level approaches 100%. The crack analysis is an important indicator that reflects the safety status in weld quality. It is also important to understand the severity of the crack formed which will help in improving the safety measures better. This will help in selecting the required materials and the proportion ratio which can handle more stress during testing. The paper is about introducing image processing techniques to analyse the crack region and perform segmentation of crack region.

Somasundaram Devaraj

Papers

Facial expression recognition techniques: a comprehensive survey

Novel deep learning model for facial expression recognition based on maximum boosted CNN and LSTM

Mathematical approach for segmenting chromosome clusters in metaspread images.

Graph Partitioning approach for Segmentation of Banding Pattern of G-band Metaphase Human Chromosomes

An Image Processing Approach for Detecting Solidification Crack in Pipeline Girth Welds