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    Emotion Detection

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    het patel
    The document discusses emotion recognition using deep neural networks and convolutional neural networks. It describes how CNNs can be used for feature extraction from facial images through techniques like convolution and max pooling. The methodology involves preprocessing input images, extracting features from facial action units, and classifying emotions. Challenges in emotion recognition from images include pose, head movements, occlusions, and ambiguity. Potential applications are discussed like using emotion recognition in cars, health, education, market research, and interviews. The conclusion discusses future scope in these domains. References include research papers and external resources on CNNs, emotion recognition, and datasets.

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    Emotion Detection

    Uploaded by

    het patel
    114 views17 pages
    The document discusses emotion recognition using deep neural networks and convolutional neural networks. It describes how CNNs can be used for feature extraction from facial images through techniques like convolution and max pooling. The methodology involves preprocessing input images, extracting features from facial action units, and classifying emotions. Challenges in emotion recognition from images include pose, head movements, occlusions, and ambiguity. Potential applications are discussed like using emotion recognition in cars, health, education, market research, and interviews. The conclusion discusses future scope in these domains. References include research papers and external resources on CNNs, emotion recognition, and datasets.

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    presentation on emotion recognition system.

    Original Title

    emotion detection

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    The document discusses emotion recognition using deep neural networks and convolutional neural networks. It describes how CNNs can be used for feature extraction from facial images through techniques like convolution and max pooling. The methodology involves preprocessing input images, extracting features from facial action units, and classifying emotions. Challenges in emotion recognition from images include pose, head movements, occlusions, and ambiguity. Potential applications are discussed like using emotion recognition in cars, health, education, market research, and interviews. The conclusion discusses future scope in these domains. References include research papers and external resources on CNNs, emotion recognition, and datasets.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    114 views17 pages

    Emotion Detection

    Uploaded by

    het patel
    The document discusses emotion recognition using deep neural networks and convolutional neural networks. It describes how CNNs can be used for feature extraction from facial images through techniques like convolution and max pooling. The methodology involves preprocessing input images, extracting features from facial action units, and classifying emotions. Challenges in emotion recognition from images include pose, head movements, occlusions, and ambiguity. Potential applications are discussed like using emotion recognition in cars, health, education, market research, and interviews. The conclusion discusses future scope in these domains. References include research papers and external resources on CNNs, emotion recognition, and datasets.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 17

    Emotion Recognition

    System
    Content
    Part 01 Introduction

    Part 02 Requirement Specification

    Part 03 Conclusion and Future Scope

    Part 04 References
    Introduction
    • As Human-Robot interaction is increasing its attention nowadays.
    • In order to put some limelight on socializing robots with humans,
    understanding the facial gestures and visual cues of an individual is a
    need.
    It allows a robot to understand the expressionof human in turn enhancing its
    effectiveness in performing various tasks.
    Socially intelligent software tools can be accomplished.
    It serves as a mearsurement system for behavioural tools.
    Honest Feedbacks Resources and
    Tools

    1 6

    ML/AI Trend
    2 Sodhyatra Counselling
    5

    3 4
    Entertainment Depression
    DNN(Deep Neural Network)
    • A neural network, in general, is a technology built to simulate the activity of the
    human brain – specifically, pattern recognition and the passage of input through
    various layers of simulated neural connections.
    • Many experts define deep neural networks as networks that have an input layer,
    an output layer and at least one hidden layer in between. Each layer performs
    specific types of sorting and ordering in a process that some refer to as “feature
    hierarchy.” One of the key uses of these sophisticated neural networks is dealing
    with unlabeled or unstructured data. The phrase “deep learning” is also used to
    describe these deep neural networks, as deep learning represents a specific form
    of machine learning where technologies using aspects of artificial intelligence
    seek to classify and order information in ways that go beyond simple
    input/output protocols.
    Emotion Recognition using CNN(Convolution
    Neural Network)
    • Convolution Neural Network or CNN as it is popularly called is a
    collection of two types of layers-
    • The hidden layers / Feature Extraction Part
    • Convolutions
    • Pooling
    • The classifier part
    • Convolution is a mathematical operation which involves a combination of two
    functions to produce a third function. In CNN the convolution is performed on the
    input data with the use of a filter to produce a feature map.
    • Pooling
    Pooling layer is added after a convolution layer. It performs continuous
    dimensionality reduction i.e. reduces the number of parameters and
    computations thereby shortening training time and controlling overfitting. One
    such pooling technique is called max-pooling, which takes the maximum value
    in each window which decreases the feature map size while keeping the
    significant information.
    Methodology

    Input storage data


    1. Static Image
    2. Sequence Video
    Pre-processing
    1. Head pose Identification
    2. Face Tracking
    3. Facial part recognition
    Feature Extraction
    1. Localization of facial action units
    2. Facial point tracking
    3. Evolving feature points Feature Classification
    1. Using Machine Learning
    2. Statistical Methods

    Emotion Recognition
    The Emotions can be classified into many type
    and can be determined through facial
    expressions.

    Happy Calm Disgusted


    Sad Unknown Angry
    Confused Fear Surprised
    Requirement Specification
    • Hardware Requirement
    • A powerful GPU/ CPU in the likes of Nvidia GeForce RTX 2080 Ti/ GTX 1060Ti, is required
    to train the model.

    • Software Requirement
    • Python language and its libraries(Numpy, Pandas, Keras, etc) are required along with
    latest IDE. To train the model, instead of our own resources, we can use cloud services
    like GCP(Google Cloud Platform), AWS, Jupyter Notebooks, Google Collab, etc.
    Challenges faced in Recognising
    Emotions

    Pose and frequent Image Orietation


    head movements and Imaging
    Conditions

    Presence of
    Occlusion Challenges structural
    components

    Ambiguity and Subtle facial


    uncertainity in face deformation
    motion measurement
    Conclusion and Future Scope

    Car System
    Car Speed can be suggested or reduced by detecting drivers mood

    Health
    Psychiatrists to detect patients emotion for better treatment

    Schools and colleges


    For counselling of students

    Market Research Companies


    For getting honest feedbacks

    Company Interviewer
    For questioning the applicant by detecting mood.
    References
    Research Papers
    Sr. No. Name Publisher Date Method Dataset
    1. Automatic Emotion Monika Feb-2016 DNN(Deep MUG facial
    Recognition Using Dubey Neural expression
    Facial Expression: A Networks), database,
    Review Prof. Lokesh SVM(Support PIE face
    Singh vector image
    machine), database,
    LDA learning JAFFE and
    algorithm and MIT+CMU
    MspLBP database
    features
    2. A Brief Review of Byoung Jan-2018 convolutional JAFFE and
    Facial Emotion Chul Ko neural MIT+CMU
    Recognition Based networks, database,
    on Visual facial action BU-3DFE,
    Information coding MMI, Nova
    system, Emotions
    facial action
    unit,
    SVM
    Books/Courses/Articles

    Sr. No. Name Platform Author/Instructor Topics


    1. Python Crash Self Eric Matthes Python Crash
    Course: A Hands- Course for
    On, Project-Based Beginners
    Introduction to
    Programming

    2. Understanding of Medium Prabhu Convolution


    Convolutional neural
    Neural Network networks
    (CNN)

    3. Introduction to edx Microsoft Python


    python fundamentals
    4. Neural networks Coursera Andrew Ng Concepts of
    and deep learning deep learning,
    Neural
    networks
    Datasets

    Sr. No. Name Description


    1. Extended Cohn- 123 subject, 600 images.
    Kanade Datasbase
    2. MMI Facial expression 75 subject, 2900 videos (extract frames from these videos to get
    detection single images)
    3. Belfast Database There are 3 sets in this database. Set 3 has HD resolution
    4. Face Detection in A simple, yet useful dataset, Face Detection in Images contains just
    Images with Bounding over 500 images with approximately 1,100 faces already tagged
    Boxes with bounding boxes.

    5. UMDFaces The UMDFaces dataset has over 367,000 face annotations across
    over 8,200 different subjects in still images. Apart from those
    images, the dataset also includes over 3.7 million video frames all
    annotated with facial keypoints of over 3,100 subjects. It should
    be noted that this dataset is strictly for non-commercial research
    purposes only.
    THANK YOU

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