Discover how deep learning is revolutionizing the way machines understand human emotions! In this presentation, we explore the cutting-edge techniques used in Facial Emotion Recognition (FER), including convolutional neural networks (CNNs), real-world applications, datasets, challenges, and future trends. Whether you're an AI enthusiast, researcher, or industry professional, this guide will help you dive deep into how technology is making emotional intelligence possible for machines. Get ready to uncover the science behind smiles, frowns, and everything in between!

1. FacialEmotionRecognition
UsingDeepLearning
Jaimy Susan Varghese

2. Introduction to Deep Learning
 Deep learning is a technique which basically mimics the human brain.
 It is a subset of machine learning based on artificial neural network.
 It is called deep learning because it makes use of deep neural networks.
 Types of DL models include Artificial Neural Network(ANNs), Convolutional Neural
Networks (CNNs), Recurrent Neural Networks (RNNs).

3. Neural Networks and Architecture
The building blocks of deep learning are neural networks. These networks consist of
layers of nodes or neurons.
 Input Layer: Receives the input data
 Hidden Layers: Processes data through multiple layers to extract features
 Output Layer: Produces the final prediction or classification

4. Introduction to Facial Emotion Recognition
 Facial emotion recognition is the process of identifying human emotion.
 In the facial recognition application faces are matches with the available dataset to
find out the human faces
 These application are mostly used in the biometric, government cyber security
areas and digital cameras.

5. The Dataset
With respect to the tasks to recognize expression, a dataset is required to train and test
the network, which has the following requirements:
 Data should be in the form of images in which most of the complete face is visible.
 The faces must be mostly front-facing.
 The resolution of images must be sufficiently large.

6. Building Facial Emotion Recognition
The libraries used for facial emotion recognition are:
 Keras : An open-source library that provides a python interface for artificial neural
networks.
 TensorFlow :A ​
free and open-source software library for machine learning and artificial
intelligence.
 OpenCv : An open-source library that can be used to perform tasks like face detection,
objection tracking, landmark detection.
 Numpy : Used to perform a wide variety of mathematical operations on arrays.
 Matplotlib: Library for creating static, animated, and interactive visualizations in Python.

7. Convolutional Neural Network​
 A Convolutional Neural Network (CNN) is a type of deep learning neural network that is
well-suited for image and video analysis.​
 CNNs use a series of convolution and pooling layers to extract features from images and
videos, and then use these features to classify or detect objects or scenes.​
 Scene labeling, objects detections, and face recognition, etc., are some of the areas
where convolutional neural networks are widely used. ​

8. SYSTEM ARCHITECTURE

9. RESULT

10. APPLICATION
 Cameras or in Cell phone camera applications​
 For the Advertising​
 Learner emotion detection​
 Attitude and Action detection​
 Marketing and scientific research​
 Face analytics and emotion recognition ​
 Film industries

11. CONCLUSION
By using the dataset the neural network will be trained and by adding maximum hidden
layer i.e with deep layer in convolutional network the result will be calculated. It covers the
concept of facial emotion recognition with aimed to classify images of faces into any of
seven discrete emotion or face expression categories that represent universal human
emotions. Advances in this field contribute to creating more intelligent and emotionally
aware systems that can enhance user experiences and provide valuable insights.

12. Let’s
Innovate
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