Facial recognition is a popular biometric recognition technique. It is commonly used to recognise the faces of persons whose images of facial features are saved in a database. This biometric method concerns the identity process and verification of an individual using their face. The facial details of an individual are captured, analysed, and compared on patterns to match the sample image saved on a database.
Facial Recognition Approaches
The different types of face recognition methods include geometric based or template-based facial recognition, piecemeal or wholistic facial recognition, appearance-based or model-based, and Neural Networks based. A brief overview of these methods is discussed in this section.
Geometric or Template-Based Approach
The geometric-based or template-based method can be constructed by utilizing popular statistical tools. This algorithm study local facial features of an individual and their geometric shape. Some examples of statistical tools used are the Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Trace Transforms, Principal Component Analysis (PCA), and Kernel methods, among others.
Piecemeal or Holistic Approach
On the other hand, a piecemeal or wholistic facial recognition method pertains to processing facial features autonomously. This explains that faces can be identified with little information from its most relevant characteristics. Many facial recognition developers follow this method. In other words, facial elements are processed holistically in this approach.
Model-Based and Appearance-Based Approaches
Next, the third method follows the idea that facial recognition can be distributed into two categories: the appearance-based or model-based approaches. It also states that the differential element of appearance-based and model-based methods is a representation of an individual’s face.
To break it down, appearance-based methods pertain to the representation of a face in terms of several raw intensity images. In this method, an image is examined as a high-dimensional vector. To explain further, researchers use statistical methods to derive a feature space from the image distribution of the individual’s face. Then. the sample image is compared to the training set.
The model-based approach attempts to model the face of a human. The new image sample is overlayed to the model. Then, the indicators of the fitted model will be used to identify and recognize the image.
Statistical, Template, Neural-Network Approach
On this approach, it explains that there are three main groups of facial recognition methods. The first category is template matching. In this category, it explains that patterns are represented by textures, models, curves, samples, even pixels. Correlation or distance measure is the common recognition function used. Second, the statistical approach‘s patterns recognition function is a discriminant function. The patterns are often represented as features. Lastly, the neural network category states that a pattern’s representation may vary. However, there is a network function at some point.
The development of facial recognition techniques is continuous. It is one of the commonly utilized biometric systems as its identification and verification require no physical interaction. It is one of the lease intrusive biometric systems. Advanced facial recognition systems can be used in screening unwanted person in-crowd in real-time. Integrating this to an establishment’s visitor management system can increase physical security in the building. To learn more about the right biometric system that fits your organization, contact Qbasis today.