Among other tasks made possible through machine learning algorithms, face detection and recognition is a crucial computer vision task. To begin with, both face detection and recognition are co-related yet colloquially different. Face detection is a wider aspect than face recognition and is applied with the help of machine learning. Whether it is about face detection in surveillance, mapping images for medical diagnosis, or deep analysis of human faces in videos for intelligence purposes, ML models apply for both detection and recognition purposes.
Evolution: Machine learning approaches for face detection & recognition
Face detection is considered a part of the computer vision domain with relation to AI or Artificial Intelligence. Depending upon the dynamic nature of the face, studies have led to application of multiple face detection ML approaches such as Viola Jones algorithm, Kohonen approach, Local Binary Pattern Histogram (LBPH), Eigenfaces, Karhunen-Loeve detector. These approaches have helped in refining machine learning algorithms, which are today used in many face detection softwares and combined with ML models to provide accurate results. Let us have a look at them.
Eigenfaces: One of the earliest face recognition approaches includes Eigenfaces. When studies into the techniques for face detection started brewing, the Eigenfaces method was adopted. The Eigenfaces approach to face detection and recognition was based on the 2D perspective. It applied the covariance matrix based on probability distribution on a high dimensional vector space to produce Eigenvectors, basis of which face variations were produced for training. Later on, the approach was used with PCA or principal component analysis (a mathematical calculation), which defines points (for analysis) pertaining to the principal components of the data. The Eigenface approach made face detection little challenging due to its sensitivity to facial changes, lighting and environment.
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