😃Facial recognition in Python can be performed using a face recognition library.
📷The library uses the Histogram of Oriented Gradients (HoG) method to find faces in an image.
📈The library enhances the face images to make them more centered and aligned.
🤝A neural network is used to generate encoded features from the enhanced face images.
🔒The encoded features are used to differentiate between different people and measure the similarity of faces.
What is the process of facial recognition with high accuracy?
—The process involves finding faces in an image using the HoG method, enhancing the face images, generating encoded features using a neural network, and comparing the encoded features to measure similarity.
What library can be used for facial recognition in Python?
—A face recognition library can be used for facial recognition in Python.
How does the library enhance face images?
—The library uses the Dlib library to find facial landmarks and align the face images to make them more centered and aligned.
What is the purpose of the neural network in facial recognition?
—The neural network is used to generate encoded features from the enhanced face images, which are used to differentiate between different individuals and measure the similarity of faces.
How can the similarity of faces be measured?
—The similarity of faces can be measured by comparing the encoded features using distance calculation methods, such as the Euclidean distance or cosine similarity.
00:00This video teaches how to perform facial recognition with high accuracy using a face recognition library in Python.
03:20The library uses the Histogram of Oriented Gradients (HoG) method to find faces in an image.
06:50The library enhances the face images to make them more centered and aligned using the Dlib library.
10:30A neural network is used to generate encoded features from the enhanced face images.
14:20The encoded features are used to differentiate between different people and measure the similarity of faces.
