Deep learning algorithms for face recognition app development
The data faceprint that stored via facial traits that are compared by the face recognition application using a deep learning process. It makes an analogy between the real-time data capture and the stored database to identify an individual. There are four main concepts subjected to deep learning for facial recognition detection of facial features, alignment, feature extraction, and recognition.
The process involved in face recognition
1. Face detection
Identify the human face in the digital images, that locate one or more faces and crop the image data and detect a visual scene.
2. Face alignment
Normalize the face to be standard with the storage device, such as geometry and photometric. It will be determined to face shape such as eye, noise.
3. Feature extraction
It’s a type of dimensionality reduction where a large number of pixels of the image are efficient that to be entitled in such a way that fascinating parts of the image are captured effectively. Extract features from the face that can be used for the recognition task.
4. Face recognition
Uses the Dlib tool to calculate the dimensional descriptor vector of face features. Whenever a vector is calculated, it is compared with the multiple referential face images by analyzing the euclidean distance to each feature vector of each Person in the database and finding an image.