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.

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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

In recent years, facial expression recognition becomes one of the interesting research topic in developing human computer interaction applications. In geometric-based methods, irrelevant features increases the computational cost of the facial expression recognition system (FERs). To overcome this issue, a geometric-based feature extraction method is proposed to extract the most relevant feature map to recognize six basic facial expressions including neutral. In this proposed method, the difference in measure of distance and triangle formation of 9 facial landmark points are calculated for each image with reference to the neutral image. Moreover, the subtle changes in facial muscle forms the deformation of facial points for different facial expressions. Hence, the difference of Euclidean distance and triangulation of each facial points of diverse expression images are calculated with respect to neutral image which are generated as feature maps and given to the SVM classifier. Further, the proposed method is evaluated using CK+, MMI and Self-collected datasets.

Saranya Rajan

Papers

Facial expression recognition techniques: a comprehensive survey

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

Geometric based Feature Extraction Techniques for Facial Expression Recognition using SVM