State-of-the-art face recognition models such as FaceNet and OpenFace rely on a specialized deep neural network architecture called siamese networks. Most recently, we’ve started to utilize deep learning algorithms for face recognition.
We simply extract features from the faces, train our classifier, and then use it to identify subsequent faces. Not only are these systems non-subjective, but they are also automatic - no hand labeling of the face is required.
However, these systems were often highly subjective and prone to error since these quantifications of the face were manually extracted by the computer scientists and administrators running the face recognition software.Īs machine learning algorithms became more powerful and the computer vision field matured, face recognition systems started to utilize feature extraction and classification models to identify faces in images. Face recognition is thus a form of person identification.Įarly face recognition systems relied on an early version of facial landmarks extracted from images, such as the relative position and size of the eyes, nose, cheekbone, and jaw. Looking for the source code to this post? Jump Right To The Downloads Section What is face recognition?įace recognition is the process of taking a face in an image and actually identifying who the face belongs to.
0 kommentar(er)
