MADHAN R
Final Project
3/21/2024 Annual Review
PROJECT TITLE
FACE DETECTION
USING CNN
ALGORITHM
3/21/2024 Annual Review
AGENDA
1. Problem Statement
2. Problem overview
3. End Users
4. Solution and Value Proposition
5. Key Features
6. Modeling
7. Results
3/21/2024 Annual
Review
PROBLEM
STATEMENT
In today's digital age, the need for efficient
and accurate face detection systems is
paramount. Whether it's for security,
surveillance, or enhancing user
experiences in applications like photo
editing and social media, the ability to
detect and track faces reliably is essential.
However, existing methods may lack
robustness or speed, leading to suboptimal
performance.
3/21/2024 Annual Review
PROJECT OVERVIEW
Traditional face detection methods often rely on
handcrafted features or complex algorithms, which
can be computationally intensive and prone to errors
in challenging conditions such as varying lighting,
poses, and occlusions. Additionally, real-time
applications require solutions that can process
frames quickly while maintaining high accuracy.
Addressing these challenges is crucial to developing
an effective face detection system.
3/21/2024 Annual Review
WHO ARE THE END
USERS?
•Security and surveillance companies seeking reliable
face detection for monitoring purposes.
•Social media platforms looking to enhance user
experiences with features like automatic tagging and
filters.
•Photo editing applications requiring precise face
detection for tasks like cropping and retouching.
•E-commerce platforms interested in personalizing
user experiences through facial recognition-based
recommendations.
3/21/2024 Annual Review
YOUR SOLUTION AND ITS VALUE
PROPOSITION
Our solution offers a robust and fast face
detection system based on Convolutional
Neural Networks (CNNs). By leveraging
deep learning techniques, our system can
automatically learn discriminative features
from images, leading to improved accuracy
and robustness in face detection. The key
value propositions of our solution include:
3/21/2024 Annual Review
THE WOW IN YOUR SOLUTION
•Utilization of Convolutional Neural Networks
(CNNs) for image detection.
•Flexibility to train on custom datasets for
specific detection tasks.
•Integration of pre-trained models for rapid
deployment and transfer learning.
3/21/2024 Annual 8
Review
MODELLING
•Dataset Preparation: Curating a diverse dataset
containing images with annotated face bounding boxes.
•Model Training: Training the CNN model on the dataset
using appropriate loss functions and optimization
techniques.
•Validation and Evaluation: Validating the model's
performance on a separate validation set and evaluating
its accuracy and speed.
•Model Optimization: Implementing optimization
techniques like model pruning and quantization for
efficient deployment
3/21/2024 Annual 9
Review
RESULT
S
Our CNN-based face detection solution offers a compelling
combination of accuracy, speed, and versatility, making it
suitable for a wide range of applications. By leveraging deep
learning techniques and advanced algorithms, we provide a
reliable and efficient solution to address the challenges
associated with face detection in real-world scenarios.
Demo Link
3/21/2024 Annual 10
Review
