Face emotion recognition is the process of detecting emotions from facial expressions. Without emotions, relational communication cannot be done among people. Human emotions are playing a vital role in the day to day life. Do you look for an article regarding face emotion recognition using Python? Then this article is dedicated to you!!!

Interpersonal communication is also considering the voice pitches or tones, body gestures, and some of the non-verbal prompts. These are the cues that are widely used to showcase emotion via human faces.

Can you even imagine communication without at least any of the images? Of course, it is not at all possible. We tend to express various expressions with emotions every day. Face Emotion Recognition (FER) is the system that can accurately recognize human facial emotions.

As python is one of the best programming languages widely used by the developers and researchers for every technology despite its lenient feature? Our researchers of the concern have lighted up this article with an overview of facial emotion recognition. Come let us get into essential sections.

What is Facial Emotion Recognition?

This article is all about face emotion recognition using Python. Facial emotion recognition involves the process of identifying facial emotions through various facial expressions. Human brains as well as many more applications are highly capable of recognizing emotions expressed by humans.

It is useful to make the interaction effectively utilizing responding to the opponent’s reaction. Psychological studies are one of the major reasons behind understanding human emotions and are mostly used to enhance these emotion recognition concepts.

Face emotion recognition applications such as emotion predictors examines the facial expression with the former inputs to detect probabilities/likelihood. Here some of the emotions are stated with their likelihood variations.

Emotion – Surprise

  • Upturned Likelihood
    • Dropped Jaw
    • Widen Eyes
    • Brow Furrow / Lines
    • Inner Brow Raises
  • Lessening Likelihood
    • Brow Furrow / Lines

Emotion – Contempt

  • Upturned Likelihood
    • Brow Furrow Smirk
  • Lessening Likelihood
    • Smile

Emotion – Sadness

  • Upturned Likelihood
    • Lip Corners Depressor
    • Brow Furrow
    • Inner Brow Raises
  • Lessening Likelihood
    • Smile & Lip Suck
    • Opened Mouth
    • Lip Press & Widen Eyes

Emotion – Fear

  • Upturned Likelihood
    • Lip Stretch / Bit
    • Wider Eyes
    • Brow Furrow & Raises
  • Lessening Likelihood
    • Smile & Jaw Drop
    • Lip Corner Depressor
    • Brow Raises

Emotion – Disgust

  • Upturned Likelihood
    • Upper Lip Raising
    • Nose Wrinkles
  • Lessening Likelihood
    • Lip Suck Smile

Emotion – Anger

  • Upturned Likelihood
    • Lip Suck & Mouth Open
    • Chin Raises & Brow Furrow
    • Eye Widen & Lid Tightening
  • Lessening Likelihood
    • Smile & Brow Raise

Emotion – Joy

  • Upturned Likelihood
    • Smile
  • Lessening Likelihood
    • Brow Furrow & Raises

This is the overview of facial emotion recognition and the likelihood variations of the different emotions expressed in human faces. You may get a question about in what manner does the FER system works in fact, the upcoming section has the answer. Shall we discuss further? Come on let you people grab them and make your understanding better.

How Does Face Emotion Recognition using Python Works?

  • Step 1: Image Acquisition
  • Step 2: Detection of Faces
  • Step 3: FacialFeature Extraction 
  • Step 4: Action Unit Detection (AU Detectors)
  • Step 5: Fusion of Detected Action Units (Fusing Model)
  • Step 6: Mapping Emotions (Mapper)
  • Step 7: Emotion Recognition

Above itemized are the major steps involved in the face emotion recognition working process. For this training, faces are injected into the database for effective emotion recognition. This process is otherwise known as hybrid action unit detection which is oriented with the human face emotion recognition. 

By conducting various researches in face emotion recognition we have attained so many incredible outcomes in it besides we’ve faced so many issues and constraints in the same system. We know that it would be really helpful for the people who are not yet conducted researches in these areas. Hence, we would like to bring up the latest issues that we are faced with in face emotion recognition using Python for the ease of your understanding.

Latest Issues in Face Emotion Recognition

  • Massive Time Consumption for Testing & Training
  • Huge Memory Requirements & Complex Computations
  • Deep Learning with Resource Limitations
  • Lack of Convolutional Neural Network Theories
  • Difficulty in Hyper Parameters Selection like, 
  • No. of Layers
  • Rate of Learning
  • Convolutional Filter Kernel Sizes
  • Expensive Internal Dependencies 
  • Requires Manually Collected Labeled Datasets 
  • Entails Huge Computing Power & Dataset for Training 

The above listed are some of the issues being faced by our technical team while working on face emotion recognition. However, these constraints can be eradicated by enhancing the performance of FER systems. Face emotion recognition uses major 3 techniques to detect facial expressions.

Generally, these are how we are also performing with facial emotion recognition and we are truly getting the best results as we defined. This is becoming possible by conducting so many researches in the FER system concurrently. Now we can learn about the major techniques of FER systems.

Major Techniques for Face Emotion Recognition

  • Image Preprocessing 
  • Image Processing
  • Feature Extraction 
  • Classification of Emotions

This is how the FER systems interpret the given inputs ranging from image preprocessing to emotion classification. Image preprocessing is the technique used to evaluate and analyze the quality of the image or any input is given for face emotion recognition using python. 

Feature extraction is involving with the spectral and temporal features along with this classification of emotion is the process of classifying human emotions in various categories such as happiness, fear, contempt, anger, disgust, guilt, and so on. Here, we would like to give a comparison between the face emotion recognition techniques for your better understanding.

Steps Involved in Face Emotion Recognition Using Python Language

Comparison of Face Emotion Recognition Techniques

Neutral Face Classification using Personalized Appearance Models for Fast Robust

Emotion Detection

  • Used Methodologies
    • Local Binary Pattern KE Points
    • Patch Processing
    • Constrained Local Models (CLM)
  • Methodologies Pros 
    • These methods use preprocessing units for computation
  • Methodologies Cons
    • Low accuracy by improper CLM fittings

Dynamic Facial Emotion Recognition from 4D Video Sequences

  • Used Methodologies
    • Support Vector Machine
    • Neural Networks
    • Apex Frames
    • Euclidean Distance
  • Methodologies Pros 
    • Compared to other methods it has high accuracy
  • Methodologies Cons
    • Yet to be evaluated in real-time scenarios 

Video-based Facial Recognition using Histogram Sequence of Local Gabor Binary Patterns from 3 Orthogonal Planes

  • Used Methodologies
    • Gabor Filters
    • Support Vector Machine
    • LGBP-TOP
  • Methodologies Pros 
    • These methods are resilient & have the least complexities
  • Methodologies Cons
    • Lacks in outcome accuracy & precision 

Spatiotemporal Feature Extraction for Facial Expression Recognition

  • Used Methodologies
    • Block Processing
    • Spatio-temporal Texture Map
    • Support Vector Machine
  • Methodologies Pros 
    • Effectively performs the feature extraction based on state of art appearances
  • Methodologies Cons
    • Basic classification frameworks & yet to handle diverse head movements

The aforementioned are the various methodologies used in facial emotion recognition with the pros and cons. Further, you can select any of the methods mentioned above according to your requirements. Emotion recognition is aimed to exactly recognize the emotion even it has fusions with different forms of communication. The next section is all about the latest topics in face emotion recognition based on several formats.

Latest Topics in Face Emotion Recognition

  • Conversational based Emotion Recognition
    • This type of emotion recognition extracts the perceptions of users of social media 
    • For example, tweet and their comments from massive application users
    • This is taking input like video, audio, text for detecting various emotions conveyed 
    • Deep learning models can exactly recognize the facial expressions
    • EEG signals & facial landmarks based on face emotion expression classification gives optimum results
  • Video-based Emotion Recognition
    • Emotion is recognized in the mixture of texts (captions), image & audio data
  • Audio-based Emotion Recognition
    • Vocal signals in the audio are extracted for emotion recognition
  • Text-based Emotion Recognition
    • Text-based emotion recognition is open source for research & ease of storage
    • Easy to compress & recognize utilizing word repetitions
    • Sources of text feature extraction are dialogues & written text data  
    • Written text data represents emotion in the forms of phrases & words

The listed above are various forms of emotion recognition that can be possible to further analyze the state of mind. Our researchers in the institute are dedicated to working with the technical areas when it comes to research they perform like a workaholic. Even they are performing in every edge in pattern recognition projects

The main objective of facial emotion recognition is to observe people’s behaviors under several circumstances. In the upcoming section, we have mentioned to you some of the research areas in face emotion recognition using python.

Research Areas using Face Emotion Recognition

  • User Authentication in ATMs
  • Attendance Systems in Schools
  • Security & Accessibility System
  • Advertising & Workers Supervision 
  • Trade Crime Identification / Detection
  • Detecting Missing / Disappeared Individuals

How great the FER system is! We know that you are also getting amused about-face emotion recognition. Did you ever notice a person while working? He may look happy and sad according to his feelings even they may behave like they are ill.  

This may cause the production of an industry in an effective manner. So that, FER system based workers supervision will result in better. As this article is tilted with face emotion recognition using python, we are going to showcase the reasons for the same.

Python for Face Emotion Recognition

  • A confusion matrix is evaluated by Python’s Scikit-learn library which is used to calculate, 
  • Sensitivity
  • Recall
  • Specificity
  • Precision
  • Accuracy
  • The whole FER system is simulated by Python’s Jupyter Notebook
  • Models implementation, convolution layer integration, fitting & assembling is done by Keras that runs on top of Tensorflow

The foregoing passage itself conveyed to you the importance of using Python in face emotion recognition and we hope that you are getting the points as of now listed. We thought that it is the right time to talk about the datasets used in face emotion recognition for the ease of your understanding. Come let us also have quick insights!!

Datasets for Face Emotion Recognition

  • Facial Expression Datasets
    • JAFFE
    • FERG
    • AffectNet
    • FEI
    • Belfast
  • Non-Facial Expression Datasets
    • FaceScrub
    • CelebA
    • UTKFace
    • LFW
    • FFHQ
    • Tufts

The above listed are the facial & non-facial expression-based datasets used in the prevailing days. Along with this, we can rate these datasets by using several performance metrics. Performance metrics are predefined & can be used to evaluate the forthcoming dataset introductions according to face emotion recognition systems.

Generally, FER systems are interpreting the images dynamically to exactly recognize human facial emotions. There are 8 attributes to be considered while recognizing face emotions. They are listed below, 

  1. Age
  2. Gender
  3. Ethnicity
  4. Gaze
  5. Location 
  6. Clothing
  7. Gestures & Pose
  8. No. of Emotion Classes

These are the 8 attributes determining facial emotion recognition and these can be measured & analyzed by several metrics as listed in the following section. This is very important to know so that we are advising you to pay considerable attention here. Shall we get into that section? Come on! Let us also learn them.

Performance Metrics for Face Emotion Recognition

  • Specificity
  • True Positive Rate (Matching Score)
  • FTC Rate (Failure to Compute)
  • Positive Predictive Value
  • F1-score
  • Sensitivity
  • Precision
  • Confusion Matrix
  • Accuracy

Itemized above are the various performance metrics used to evaluate the performance of the face emotion recognition system. Finally, we hope that we’ve illustrated to you the best things with proper justifications. If you still have any doubts about the above-debated areas, you could interact with our researchers at any time.

As Python is one of the emerging programming languages used in various fields of technology, it is being suggested by our academics and even by top world-class engineers. In the end, when you are handpicking Python as a programming language for face recognition, fingerprint recognition it will offer you incredible results with diverse functionalities. Reach us if you are struggling to implement face emotion recognition using python.

Let’s crack your thoughts and perceptions to chic the FER system’s current direction into an interesting future direction