Skip to content

Latest commit

 

History

History
78 lines (51 loc) · 5.75 KB

README.md

File metadata and controls

78 lines (51 loc) · 5.75 KB
Raw

exVision: FacialSys

Overview

FacialSys is a facial system that incorporates both face detection and recognition using classical computer vision techniques. The system employs the Viola-Jones algorithm for face detection, which leverages integral images, Haar-like features, and cascade AdaBoost classifiers for real-time and robust face detection. For face recognition, the system uses Principal Component Analysis (PCA) to extract and analyze key features from facial images, enabling efficient recognition through the Eigenfaces method. The application is implemented using PyQt5 for the desktop interface, providing an intuitive environment for real-time usage.

System Components

Face Detection

We employ the Viola-Jones object detection algorithm, which uses a cascade of classifiers boosted by the AdaBoost algorithm to efficiently detect faces in real-time. This method operates as a three-stage filtering process, where each stage applies progressively more complex classifiers to quickly discard regions that do not contain a face, while focusing computational resources on promising candidates.

Cascade Classifier

The algorithm scans the image using sliding windows of 15x15, applying Haar-like features to each window to capture essential facial patterns, such as edges, lines, and textures. By leveraging integral images for fast computation, Viola-Jones can detect faces swiftly and accurately, even in varying lighting conditions or complex backgrounds.

preview of the face detection

Detection Dataset

  • For positive class ("face"), we used the Yearbook Dataset from A Century of Portraits: A Visual Historical Record of American High School Yearbooks.

    It contains 37,921 pictures of frontal-facing portraits of size $186 \times 171$ where we will assume the face to be in the center.

  • For the negative class ("not a face") we made use of the Stanford Backgrounds Dataset for scene understanding.

    It contains 715 images of various scenes, from which we will randomly crop regions to be used as background classes. The images are approximately of size $320 \times 240$ and do contain faces in camera-facing poses.

For a more in-depth understanding of the methodology and analysis, please refer to Viola-Jones.ipynb notebook in the attachment folder.

Face recognition

For face recognition, we employ Principal Component Analysis (PCA), often referred to as the Eigenfaces method. PCA reduces the high dimensionality of facial images by projecting them into a lower-dimensional space while retaining the most important features.

The face recognition process follows these key steps:

  1. Dataset Preparation: Facial images are resized and flattened into vectors, converting the 2D images into 1D arrays.
  2. Covariance Matrix Calculation: The covariance matrix is computed to identify the directions of maximum variance in the data.
  3. Eigenface Extraction: Eigenfaces, which are the principal components (eigenvectors) of the dataset, are extracted and represent the key facial features.
  4. Recognition: New faces (Queries) are projected into the PCA space and compared with known faces for recognition based on their similarity.

Face recognition

Recognition Dataset

  • The dataset we used was the ORL Database of Faces, which is quite of age.

    There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions and facial details. The size of each image is $92 \times 112$ pixels.

For a more in-depth understanding of the methodology and analysis, please refer to PCA_analysis.ipynb & Face_recognition.ipynb notebooks in the attachment folder.

Getting Started

To be able to use our app, you can simply follow these steps:

  1. Install Python3 on your device. You can download it from here.
  2. Install the required packages by the following command.
pip install -r requirements.txt
  1. Run the file with the name "facialsys_backend.py" in the app folder.

Acknowledgments

References

If you're interested in learning more about the algorithms used in this project, you can refer to the following papers:

  • Viola, P., & Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features, Available online here.

  • Kanan, C., & Cottrell, G. W. (2009). Color-to-Grayscale: Does the Method Matter in Image Recognition? Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, United States of America. Available online here.