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Face Recongnition using Machine Learning
- 1. Facial Recognition By Sarthak Roy BCA 2020-23 AIITK A91404820027 Guided By Dr. Soma Debnath
- 3. Implementation of Face Detection and Recognition by simply using Laptop's Webcam. Real Time face detection using Face Detection algorithm to visualize human faces in Digital image. We are using video feed from a webcam which are a sequence of frames of still images being updated one after the other to recognize and predict faces.
- 4. Theory
- 5. Different Facial Recognition Methods Geometric Eigenfaces Fisherfaces Local Binary Patterns Active Appearance 3D Shape Models
- 6. Geometric • First method of facial recognition • Done by hand at first • Automation came later • Find the locations of key parts of the face • And the distances between them • Good initial method, but had flaws • Unable to handle multiple views • Required good initial guess
- 7. Eigenfaces • Information theory approach • Codes and then decodes face images to gain recognition • Uses principal component analysis (PCA) to find the most important bits
- 8. Fisherfaces • Same approach as Eigenface • Instead of PCA, uses linear discriminant analysis (LDA) • Better handles intrapersonal variability within images such as lighting
- 9. Local Binary Patterns • Describes local features of an object • Comparison of each pixel to its neighbors • Histogram of image contains information about the destruction of the local micro patterns
- 10. Convolutional Neural Networks • CNNs are deep learning models that autonomously learn hierarchical features from raw image data. • They comprise multiple layers, including convolutional layers, pooling layers, and fully connected layers. • CNNs effectively capture complex patterns and variations in facial images. • They are trained on large and labelled datasets
- 11. Methodology to be used by the Project
- 12. Dlib’s face recognition built with deep learning • Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. • The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark. • It is very easy to implement as it is built in with face-recognition python module
- 13. Live Demo