Introduction
Facial landmark detection plays a crucial role in various applications, including facial recognition, emotion analysis, and augmented reality. In this assignment, students are tasked with developing a facial landmark detection system using a modified YOLO (You Only Look Once) algorithm. The goal is to accurately identify and display facial landmarks, such as the eyes, nose, and mouth, in an image. The assignment provides students with a specific version of YOLO to use, and they are expected to showcase their results through a base model and a final model. Evaluation will be based on metrics like Mean Error and Mean Average Precision, with additional performance metrics encouraged for higher grades.
Problem Statement
The objective of this assignment is to develop a facial landmark detection system using a modified version of the YOLO algorithm. Students are expected to build a base model and a final model, both of which should be saved for evaluation. The assignment requires the use of Mean Error and Mean Average Precision as performance metrics, although additional metrics are encouraged and will be graded accordingly.
Expectation
For the submission, students are required to upload their entire code, including saved files for the base and final models. Additionally, they need to provide a code snippet to load the final model, which should include a variable called "samp_img" for assessors to input the path of a sample image. The output should then display the detected facial landmarks on that image.
Resources Provided As this assignment aims to teach research methods, limited resources will be provided. The dataset for the assignment can be found at a specified source and includes train, test, and validation folders containing images, labels, and landmark coordinates. Students are encouraged to reference the provided sources, including research papers and articles related to facial landmark detection and the YOLO algorithm. They should also include a minimum of six additional references in their report.
The following is a suggested skeleton for the method to be followed in developing the facial landmark detection system using the modified YOLO algorithm:
Modify YOLOv7 for Facial Landmark Detection:
Change the network architecture by replacing the output layer with a new fully connected layer that predicts facial landmark points represented as (x, y) coordinates.
Train the network using a dataset that contains facial images paired with corresponding landmark points.
Modify the Loss Function:
Adapt the loss function to incorporate the predicted facial landmark points.
This can be achieved by using a combination of Mean Squared Error (MSE) and binary cross-entropy loss.
Post-processing:
Apply post-processing techniques, such as Non-Maximum Suppression (NMS), to eliminate duplicate predictions and refine the detection results.
Fine-tune the Network:
Further refine the network by fine-tuning it using a smaller dataset specific to the target application.
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