Face recognition systems can be used for both verification and identification of humans in biometric security systems. Face recognition algorithms are mathematical processes used for facial recognition from digital images. Face recognition systems are digital image processing engines that can be implemented using Matlab software sourcecode and DSP processor hardware. Alternatively, Face recognition algorithms/systems can be implemented in VLSI hardware using ASIC/FPGA design by Verilog/VHDL coding.

How Face Recognition Algorithms work?

Facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the person’s face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features.

Earlier Face Recognition Algorithms used simple geometric models but the face recognition process has now matured into a science of sophisticated mathematical representations and matching processes.

Types of Face Recognition Algorithms

Face Recognition algorithms can be divided into two main approaches, geometric, which looks at distinguishing features, or photometric, which is a statistical approach that distill an image into values and comparing the values with templates to eliminate variances. Popular recognition algorithms are:

  • Face Recognition using Principal Component Analysis with eigenfaces
  • Face Recognition using Linear Discriminant Analysis (LDA)
  • Face Recognition using Elastic Bunch Graph Matching (EGBM) fisherface
  • Face Recognition using Hidden Markov model
  • Face Recognition using Neuronal motivated dynamic link matching
  • Face Recognition using Skin texture analysis based on unique lines, patterns, and spots apparent in a person’s skin

Three-dimensional Face Recognition (3D Face Recognition)

Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. 3D face recognition has the potential to achieve better accuracy than its 2D counterpart by measuring geometry of rigid features on the face. This avoids such pitfalls of 2D face recognition algorithms as change in lighting, different facial expressions, make-up and head orientation.