FACIAL EMOTION RECOGNITION AND DETECTION IN PYTHON USING DEEP LEARNING

FACIAL EMOTION RECOGNITION AND DETECTION IN PYTHON USING DEEP LEARNING

ABSTRACT 

Humans often have different moods and facial expressions changes accordingly. Human emotion recognition plays a very important role in social relations. The automatic recognition of emotions has been an active analysis topic from early eras. In this deep learning system user’s emotions using its facial expression will be detected. Real-time detection of the face and interpreting different facial expressions like happy, sad, angry, afraid, surprise, disgust, and neutral. etc. This system can detect six different human emotions. The trained model is capable to detect all the mentioned emotions in real-time. An automatic facial expression Recognition system has to perform detection and site of faces during a cluttered scene, facial feature extraction, and facial expression classification. Therefore, we propose a framework of three models: CNN (deep learning algorithm), SVM (machine learning algorithm), and a novel ensemble learning algorithm and PCA as our dimensionality reduction function. The performance of our models was evaluated on JAFFE, CK+ and KDEF datasets. It was observed that our proposed models outperformed the state-of-the-art methods, with mention of our novel ensemble learning model which attained an accuracy of 100% on the JAFFE dataset.Thus we conclude that the proposed methods are effective for the recognition of anger using facial expressions and future work will look at evaluating the performance of these algorithms on a created database of Africans as well as employing these algorithms to detect anger in a persuasive space and persuade the individual from angry to another emotion for example happy.

 

 

CHAPTER ONE

INTRODUCTION

• Background

The interest in human emotions has spanned many centuries. Darwin’s influential work on the expression of the emotions in man and animals served as the starting point for emotion research (Darwin, 1872; Petrushin, 2000). Subsequently, there has been a significant contribution from multidisciplinary fields such as psychology, computer science, medicine, sociology and so on (Mitsuyoshi & Ren, 2013).

Facial expression is a key factor in human communication revealing an individual’s thoughts and emotions naturally during communication (Jameel, Singhal, & Bansal, 2016). Therefore, it can be concluded that the face is an important feature of the body as it conveys an individual’s personality, emotions, thoughts and ideas even before it has been verbalized; playing a significant role in human communication and social interaction (Dhall & Sethi, 2014; Mitsuyoshi & Ren, 2013).

As such, the invention of human-centred interfaces by the next generation computing namely: persuasive computing has brought immense benefits which project the human user to the foreground. This next-generation computing readily responds to human communication, as these interfaces can perceive and understand human emotions and intentions which are communicated by the social and affective signals (Pantic, Pentland, Nijholt, & Hunag, 2007). These human-computer interaction interfaces seek to reshape the behaviour and intentions of individuals as well as seek to improve their health; hence the proposal of the construction of persuasive spaces or the use of persuasive technology to change an individual behaviour or emotion to a predetermined one (Stibe & Wiafe, 2018). Inspired by this vision, the field of human-computer interaction, computer vision and pattern recognition has witnessed colossal transformation including the automated analysis of facial expressions or facial behaviour with deep learning algorithms.

• Statement of Problem

mage recognition, generally, faces a number of problems in implementation, some of these issues, as discussed by other researchers, are broadly; computer resources (memory), speed, accuracy, hard coded solution (non-generic), etc. Although various facial recognitions  algorithms have been invented and used for the recognition of the basic emotions or a subset, some problems persist which need to be addressed because majority of the facial recognition algorithms have difficulty in performing a multi-class classification, which is attested to with regards to the training time, computational time and the insufficient memory space (Kiran & Kushal, 2016; Shah, Sharif, Yasmin, & Fernandes, 2017). Hence, this study intends to develop a facial emotional recognition and detection in python using deep learning.

• Aims and objectives

The aim of this study to develop a facial emotional recognition and detection in python using deep learning. Other objectives include:

1. To research on emotions and determine the significant measure for the measuring of emotions.
2. To research literature on facial expression algorithms.
3. To investigate and understand the algorithms used for facial expressions recognition

(FER).

4. Implement deep learning techniques into frameworks for facial emotions recognition.

 

• Scope of the Study

The proposed framework will employ both deep learning algorithm complemented with machine and ensemble algorithm method for our work. Our proposed models, in general, outperformed the state-of-the-art methods.

• Structure of the thesis

The thesis is organized as follows:

Chapter 1 gives a succinct overview of the background of the study, the problem statement, aims and objectives and expected contribution.

Chapter 2 presents a review of the different algorithms at the three stages in facial image recognition and provide insight into the background of emotion research as well as some psychology emotions theories.

In chapter 3 of this study, the experimental methodology is described. It involves the exploratory discussion of our different methodologies and datasets.

Chapter 4 details the implementation process. It presents details on the experimental setup, preprocessing, feature extraction and selection and classification.

Chapter 5 deals with the conclusions, as well as a summary of the work, are drawn from the thesis.

 

FACIAL EMOTION RECOGNITION AND DETECTION IN PYTHON USING DEEP LEARNING

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