Overview

• Geometric or Template-Based Approach
• Piecemeal or Holistic Approach
• Model-Based and Appearance-Based Approaches
• Statistical, Template, Neural-Network Approach

Facial recognition is a popular biometric recognition technique. Commonly, big businesses use this to recognise the faces of persons where the database saves images of facial features. This biometric method concerns the identity process and verification of an individual using their face. Facial recognition devices capture, analyse and compare facial patterns and save them into the 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. This section discusses a brief overview of these methods.

Geometric or Template-Based Approach

The geometric-based or template-based method constructs tools by utilizing popular statistics. This algorithm studies the local facial features of an individual and their geometric shape. Some examples of statistical tools used are 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 holistic facial recognition method pertains to processing facial features autonomously. This explains that faces can be identified with little information from their 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: appearance-based or model-based approaches. It also states that the differential element of appearance-based and model-based methods are 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. This method examines an image as a high-dimensional vector where researchers use statistical methods to derive a feature space from the image distribution of the individuals where the sample image compares to the training set.

The model-based approach attempts to model the face of a human. The new image sample overlays on the model. Then, the fitted model uses the indicators to identify and recognize the image.

Statistical, Template, Neural-Network Approach

This approach explains that there are three main groups of facial recognition methods. The first category is template matching. It explains patterns that represent textures, models, curves, samples, and even pixels. Commonly, the recognition function uses correlation or distance measures. Second, the statistical approach‘s pattern recognition function is a discriminant function where patterns often represent 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. One of the most commonly utilized biometric systems as its identification and verification requires no physical interaction. It is one of the least intrusive biometric systems. Advanced facial recognition systems screen unwanted people in-crowd in real-time. Integrating this into 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.