International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
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EMOTION RECOGNITION SYSTEMS: A REVIEW Shilpa M1, Prof. Hema S2 1PG
Student, Dept. of Electronics & Communication Engineering, LBSITW, Kerala, India Assistant Professor, Dept. of Electronics & Communication Engineering, LBSITW, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Emotions are state of feelings that can be associated with certain situations. Emotion recognition plays an important 2
role in today’s world. It has been an important research area in the recent years. It has a wide range of applications in the field of healthcare, biometric security, education etc. Emotions can be recognized through handwriting, facial expression, speech, posture etc. Different methods can be used for emotion recognition based on its application. This paper gives a brief review of some existing emotion recognition methods by some deep learning and machine learning techniques. The features extracted and the algorithms used in each paper were also briefly discussed.
Key Words: Convolutional Neural Network (CNN), Mel Frequency Cepstral Coefficients (MFCC), Emotion recognition, Support Vector Machine (SVM), Recurrent Neural Network (RNN)
1. INTRODUCTION Emotions are associated with one’s thoughts, feelings, responses, pleasure etc. There were large range of emotions that can be seen in each individuals. It can vary depending on a situation. Emotion recognition is gaining popularity day by day. Applications of emotion recognition includes in the field of medicine, e-learning, monitoring, entertainment, marketing, customer services, security measures etc. Artificial Intelligence (AI) is a technology that makes smart machines capable of performing tasks that require human intelligence. The availability of large quantities of data and new algorithms made AI an emerging research area in recent years. Through AI, it is possible to recognize emotions by various algorithms. Emotional state of a person can be accessed through various ways such as by handwriting, facial expressions, voice analysis, ECG signals, body postures, etc. The main steps involved in emotion recognition: 1) Input feature extraction 2) Emotion classification. Features extracted for each method varies depending upon the input provided for emotion classification. This paper presents a review of emotion recognition systems through various machine learning and deep learning methods.
2. REVIEW ON EMOTION RECOGNITION SYSTEMS Akriti Jaiswal et al. [1] proposed a facial emotion detection using deep learning. Here the images were given as an input to a CNN network. Feature extraction was done by two submodels by sharing the input and they were of same kernel size. The output obtained through it were flattened into vectors and it is given to a fully connected layer which will classify the emotions. A. Christy et al. [2] proposed an emotion recognition through speech signals. Here the speech signals splits into short frames. Then feature extraction from each frame was performed using MFCC and Modulation Spectral features. Then the extracted features were used for the classification of emotions. Here the classification was done by using decision tree, random forest, SVM and CNN. CNN has shown more accuracy in recognizing emotions compared to others. Here only limited samples were taken. Dhara Mungra et al. [3] proposed an emotion recognition system through facial expressions. Emotion recognition was performed initially by some specific image pre-processing steps and by using CNN. This method uses haar cascade for face detection and histogram equalization for increasing the contrast of the image. Also data augmentation was done subsequently for increasing the size of the dataset. Then the images were given to the CNN model for the classification of emotions. This model gives more testing accuracy when using both histogram equalization and data augmentation than without using both histogram equalization and data augmentation.
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