The term face emotion recognition refers to the method in which human beings’ emotions (e.g. Happy) are recognized in the face. The most significant expression conveyed by the individuals is happiness. Matlab is the best tool used for face emotion recognition. Face emotion recognition is often called FER. “Say goodbye to your mix-ups and dilemmas in the areas of face emotion recognition using Matlab thus this handout is all about the interesting concepts of the same with crispy illuminations”
Generally, humans’ facial expressions are analyzed due to various reasons in several circumstances. For instance, the genuineness of the people is often examined in the crime detections by detecting their expressions minutely. It is not easy to detect micro-expressions through a system that will require detecting facial expressions. When using Matlab as the tool in FER will abundantly give the results with high accuracy.
Are you feeling very clumsy about the concepts in FER? Don’t feel calcified here is a blissful thing for your thought-provoking. Yes, we are talking about this handout. Let’s make refresh your mind with interesting content treasured in this handout. Come on guys; let us get into the important sections of FER.
How Does Face Emotion Recognition Work?
Conventional Face Emotion Recognition Approaches
- Facial Feature Detection
- Eyes
- Eyebrows
- Nose
- Mouth
- Feature Extraction
- Temporal Features
- Spatial Features
- Expression Classification
- Random Forest
- AdaBoost
- Support Vector Machine
Latest Face Emotion Recognition Approaches
- Emotion Regulation
- Color Actuation (Emitted Lights)
- Musical Actuation (Electrical Devices)
- Emotion Feedback / Response Control
- Decision Making
- Multi-modal Separation
- Emotion Detection
- Multi-modal Fusion (Valence / Arousal & Physiological Sensor)
- Data Acquisition
- Feature Extraction from Multiple Inputs
- Processing of Features from Multiple Inputs
- Classification of Fused Features
- Emotion Classification (Facial Emotion & Behavior)
- Image Acquisition
- Facial Point Detection
- Image Segmentation
- Facial Feature Extraction
- Classification of Emotions
This is how the emotions are recognized by the features acquired in both conventional & latest approaches. Some of the devices’ deployment will alarm the condition of the projected person.
On the other hand, face emotion recognition using matlab can be applied to any of the fields either technical or non-technical. We know that you are interested to know the FER’s applicable areas. For the ease of your understanding, here we would like to cover the next section with its illustrations.
Face Emotion Recognition Applications
- Crime Identification
- Spots out thieves
- Detects politically aware arrogances
- Detects driver’s low energy
- Fraud prevention systems
- Identifies & decreases forged insurance claims
- Public Safety
- Inspects crime scene footages for crime motives
- Smart boundary controls & untruth detectors
- Screening of public places to detect terrorism threats
- Education
- Identifies student’s engagement in virtual learning
- Models emotional teaching system
- Responses based learners learning track
- Observes students’ responsiveness
- Employment
- Observes attention & mood swings of employees
- Assists to decision making
- Spots unresponsive applicants in interviews
- Medicare
- Prevents suicides
- Patient condition monitoring
- Aged people’s depression level identification
- Mental disorders prediction & assistance
- Identifies neurodegenerative & autism syndromes
- Personalized Service Provision
- Individual reaction prediction in movies, shops & ads
- Facial expression tracking for marketing motives
- Analyzing customers’ emotions in shopping
- Personal recommendations in e-commerce
- Analyzing the state of mind to display personalized messages
The aforementioned are the 6 basic areas of FER application. The outcome given by face emotion recognition is highly précised in nature. They keenly observe the human expressions in the feature by feature. Before learning about the features, primarily you need to know about the issues that arise while recognizing the emotion.
In the immediate section, we have listed some of the minute emotions of the human being with the descriptions. Our experts of the concern have concentrated more on this article to effectively fill your brain with worthy keynotes. Shall we get into the next section guys? Come on, dear students!!!
What are the Issues in Face Emotion Recognition?
- AUs – Action Units
- Encodes the muscles & individuals actions when they realize the facial expression
- This will happen according to the single emotion expressed
- CEs – Compound Emotions
- It has 22- 25 compound emotions & 7 basic emotions
- Basic emotions such as joy, anger, surprise, fear, sad, hate, curiosity
- MEs – Micro Expressions
- These expressions are expressed against your will naturally or indirectly
- They expose the honesty of the person in a fraction of seconds
Are you thinking that how the topic is relating to these expressions? That’s right!!! Here we have the corresponding justification for you. As we said that, these are the minuscule expressions that cannot be even noticed by the person who is standing near to that intended person. Thus this makes a big question mark to the face emotion recognition using matlab when it comes to minute.
Our technical experts felt that it would be better to explain further for you better understanding in this core area. Hence, we planned to describe one of the above-listed expressions in the forthcoming section. Come on dears, it is the time to educate yourself.
Micro-Expressions for Emotion Recognition
The expressions which are expressed for a fraction of a second are known as micro-expression. They are invisible to the immediate bystanders. This is happening for every single half-second. Micro-expressions always convey the same feeling everlasting.
On the other hand, they are interconnected with emotions using exposing the people whether they are hiding something (feeling) or deceitful. Understanding & highlighting the micro-expressions are complex.
Learning about micro-expression can deeply make your understanding better in the emotion of recognizing others. Expressions are identified by the features placed on the human faces.
As you know that, human faces are featured with some sensible features such as eyes, eyebrows, mouth, nose, and so on. These are the features that are helping the face emotion recognition system to exactly identify the emotion expressed by an individual. Let us discuss further explanations in the next phase.
What are the Features for Face Emotion Recognition?
- Mouth
Humans habitually hide other emotions by their fake smiles. This can be identified by an eye’s micro-expression which always shows genuine feelings. They consider some of the positions of the feature to exactly identify the state of feeling as mentioned below,
- Raised mouth’s single side – disgust or dislike
- Downed mouth curves – depressed & unhappiness
- Upturned mouth corners – happy
- Dilated mouth – anxiety & fear
- Plunged jawlines – surprise & wonder
- Casing mouth – concealing feelings
- Biting lips – nervousness
- Tightened lips – dislike & distaste
- Eyebrows
Eyebrows of the human can reveal the personal emotions of the intended person. In addition, they are very important for emotion recognition. The state of eyebrows positions can be considered as mentioned below,
- Drained inner bends – sadness
- Raised curves – wonder
- Joined & dropped curves – panic, anger, trouble
- Eyes
Eyes are the illustrating feature of human beings who can see the state of others’ feelings. Further, actions of the eyes can be well thought as,
- Strong eyeing – thoughtfulness/hatred
- Staring away – worry/diversion
- Quick blinks – sorrow / distress
- Short blinks – controlling eyes
- Opened eyes – excitement / curiosity
The foregoing passage has contributed some of the domineering stuff by revealing the sensible features in which manner they are recognizing the emotions. The above listed are the major features that can convey the expressions of humans.
On the other hand, several methods are proposed and practiced in FER systems without these methods they emotion recognition will have flopped. So that it is very important to get to know about the methods. Moreover, we’ve framed the article’s next section with the same for the ease of your understanding.
General Methods for Face Emotion Recognition
- Template Matching
- Gender Classification
- Eigenface
- Morphological Image Processing
- Color Segmentation
These are the methods that are very commonly used by world-class engineers for recognizing the emotions expressed in human faces. Also, we need to consider the improvements of face emotion recognition. This can be attained by accommodating several aspects as listed in the subsequent section.
How to Improve the Performance of Face Emotion Recognition using Matlab?
- Use of Eigenface based facial emotion recognition
- Use of High dimensional images (3D)
- Use of Pose invariant image illustrations (Color Histogram)
- Use of Compound registration in different locations ( Storage & FAR)
These are the ways to improve the performance of face emotion recognition processes in general. Besides, here you might need some clarity about at least one of the above listed. We are also planning to showcase 3D-based facial emotion recognition. They completely give their major contributions in recognizing facial emotion as mentioned below.
- Even compatible with alterations like glasses, beards & expression
- Upsurges the accuracy level
- Eliminates lighting & pose anomalies
This is how the 3D-based FER benefits us while processing. Our academics in the concern are well versed in the concepts of FER technologies. You may think that, how they are performing every technical edge, the answer is we are engaged and scheduled our worthy time in various researches.
This article is concentrated on giving face emotion recognition using Matlab, where we wanted to number out the toolboxes used for FER techniques. This would be going to help you a lot while conducting your researches in these areas.
Matlab Toolboxes for Face Emotion Recognition
- ‘Machine Learning’Toolbox
- ‘Image Processing’ Toolbox
- ‘Image Acquisition Toolbox
- ‘Deep Learning’ Toolbox
The listed above are the major toolboxes of Matlab used for recognizing the emotion expressed in human faces. Do you know that? We are exclusively going to explain how the functions are processed in Matlab with clear explanations. This is only for you!!! Let’s sail with us to get the ever-told Matlab functions.
Steps for Face Emotion Recognition Using Matlab Software
- Step 1: Image selection
- Reads the given image input
- Step 2: Adding selected images into the database
- Stores the selected images in the database & trains them
- Step 3: Face express recognition
- The selected image is processed to recognize the expression
- Step 4: Database information
- Illuminates the complete details of the database
- Step 5: Removal of database
- Terminates the prevailing database from the directory
- Step 6: Program information
- Showcases the information regarding software
- Step 7: Source code of the process
- Emphasizes the source code be used
- Step 8: Exit
- Exiting from program (quit)
This is how the Matlab functions processes when an image is given to it. This is just a sample, you may get wondered the Matlab function if you experience it. In the upcoming section, we have also put on a display of Matlab functions to retrieve the proposed recognition. Hmm! yes, dears we are exactly going to let you how to run a script for facial expression recognition for the ease of your understanding.
function add_Detections (obj, I, bbox)
- Identifies if a detection either belongs to a prevailing face, or
- If it belongs to the newfangled face
function selected_bbox = predict_face (img_rz, .mfile)
function output = predict_emotion (img, .mfile)
function face_gender_age_emotion_detection (detect_emotion, detect_gender, detect_age)
function face_gender_age_emotion_detection_on_jetson (detect_emotion, detect_gender, detect_age)
These functions signifies the deep neural networks use cases as,
- Detects the faces
- Classification of detected faces as female & male
- Assumes & predicts the age of detected faces
- Recognizes the emotion of detected faces
function (test_data, classes) = test_data (i, j, shapes_fear, shapes_disgust, shapes_anger, shapes_sadness, shapes_surprise, shapes_happiness, shapes_neutral)
function feature_vectors = construct_hf (map, input_vectors)
This function summarizes the following,
- Input vectors are D bins’ N histograms & NxD arrays
- LBP histogram based rotation invariant features are built uniformly
So far, we have gone through the extents & functions of FER. Generally, face emotion recognition using the Matlab tool is blindly trusted for their accuracy levels. Hence, we are suggesting you do the researches in the same to get incredible results. Our technical crew is familiar to implement Face Emotion Recognition using Matlab tool so there is nothing to worry about when you struck on.
“Spending your time and showing your strength of mind will determine your effective outcome in the proposed empirical areas”
