Face Recognition Final Year Projects with Source Code

Byadmin Final Year Projects

Face Recognition Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Face Recognition projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Face Recognition Final Year Projects

  1. Analysis of Facial Expressions to Estimate the Level of Engagement in Online Lectures
    This study developed a method to estimate how attentive students are during online lectures by analyzing their facial expressions. Researchers measured reaction time to sounds that were unrelated to the lecture and assumed slower reactions meant higher focus. They used a machine learning model to predict reaction times from facial movements. The results showed that facial expressions can reliably indicate students’ attention, even when they are not sleepy.
  2. Computer Vision-Based Assessment of Autistic Children Analyzing Interactions Emotions Human Pose and Life Skills
    This project uses computer vision and deep learning to analyze videos of children with Autism Spectrum Disorder during play sessions. It tracks their movements, emotions, and interactions with therapists to understand social skills and attention. The system can automatically recognize joint attention, activities, and facial expressions with high accuracy. This helps clinicians assess, monitor, and plan treatments for children with ASD more effectively.
  3. Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques
    This project uses computer vision and machine learning to understand students’ emotions in a classroom. It tracks facial gestures to identify feelings like interest, boredom, or enthusiasm during learning. The system builds a database of real, spontaneous emotions and helps teachers evaluate students’ emotional engagement along with their learning progress. It focuses on supporting teachers in face-to-face education.
  4. Brain Age Prediction Based on Resting-State Functional MRI Using Similarity Metric Convolutional Neural Network
    This project focuses on predicting a person’s brain age using MRI scans. It uses a special type of neural network to compare brain images from different people and measure their similarity. The model learns important features from the images and predicts brain age with good accuracy. Tests show it works well on a dataset of brain scans over time.
  5. A Systematic Review of Facial Expression Detection Methods
    This project studies how computers can recognize human emotions from facial expressions. It reviews many research studies that use deep learning techniques, especially convolutional neural networks. The work compares different methods and datasets to see which are most accurate. It helps understand which AI models work best for emotion detection.
  6. Facial Expression Transfer Based on Conditional Generative Adversarial Networks
    This project focuses on transferring facial expressions from one face to another using advanced computer vision. It uses a special neural network model that combines key facial features from a source and target face. The model creates realistic images that keep the target person's identity while showing the new expression. Experiments show it works better and faster than previous methods.
  7. Generalization of Forgery Detection With Meta Deepfake Detection Model
    This project focuses on detecting fake videos and images created by face manipulation. It uses a deep learning approach that can learn from multiple sources and adapt to new, unseen types of fake media. The model trains in a way that improves its ability to generalize, so it can detect deepfakes without needing updates for each new type. Overall, it aims to make face forgery detection more reliable in real-world situations.
  8. Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface
    This project focuses on automatically recognizing human emotions from facial expressions. It uses deep learning to analyze faces and detect different emotions. The system removes noise from images, extracts important features, and trains a model to classify emotions accurately. Tests show it works very well, reaching about 99% accuracy.
  9. Model Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors
    This project studies how to make deep learning models focus better on important parts of an image. The researchers use human perception to guide the model’s attention during training. They introduce new ways to reduce randomness in where the model looks, which improves its performance on unfamiliar data. Experiments on synthetic face detection show that models trained this way detect faces more accurately than standard methods.
  10. Enhanced Artificial Vision for Visually Impaired Using Visual Implants
    This project aims to help visually impaired people see better using retinal implants. It uses a camera on glasses to detect people nearby, their age, gender, emotions, and distance. This information is then converted into simple visual patterns that the implant can show to the user. The system works on a small, low-cost device and can run in real time, making it practical for daily use.
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Face Recognition Project Synopsis & Presentation

Final Year Projects helps prepare Face Recognition project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Face Recognition Project Thesis Writing

Final Year Projects provides thesis writing services for Face Recognition projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

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