Face Recognition Technology: A Review
Jagdish Chandra Joshi, Karunesh Kumar Gupta · SSRN Electronic Journal · 2016
The paper reviews face recognition techniques—an actively researched area in the field of biometrics, pattern recognition and computer vision. Maiden attempts were made in the early 1960s or so, but significant progresses were made only around 1988, in synchronization with a massive increase in computational power. The first widely accepted algorithm was the Principal Component Analysis (PCA) or Eigenface method, which even today is used as the most significant tool for dimensionality reduction. Today, many scientists agree that the case of any two simple facial images, under well-controlled conditions of environmental constraints/variables, for comparison is practically known to be a solved problem. Even with minimal variations in such images, apart from facial expression, the problem is insignificant by today’s standards with a recognition accuracy of around 90-98% reported across many papers. This is arguably even better than human performance in the same conditions provided, when humans are tested on the images of the unknown persons. However, when variations in images caused by pose, aging or extreme illumination and pattern recognition conditions are introduced, the ability of human beings to recognize faces is still remarkable as compared to computers.