Face Recognition For Beginners

Face Recognition is a recognition technique used to detect faces of individuals whose images saved in the data set. Despite the point that other methods of identification can be more accurate, face recognition has always remained a significant focus of research because of its non-meddling nature and because it is people’s facile method of personal identification.

Face Recognition Methods: -

There are different methods for face recognition, which are as follows-

1.Geometric Based / Template Based: -

Face recognition algorithms classified as geometry based or template based algorithms. The template-based methods can be constructed using statistical tools like SVM [Support Vector Machines], PCA [Principal Component Analysis], LDA [Linear Discriminant Analysis], Kernel methods or Trace Transforms. The geometric feature based methods analyse local facial features and their geometric relationship. It is also known as a feature-based method.

2.Piecemeal / Wholistic:-

The relation between the elements or the connection of a function with the whole face not undergone into the amount, many researchers followed this approach, trying to deduce the most relevant characteristics. Some methods attempted to use the eyes, a combination of features and so on. Some Hidden Markov Model methods also fall into this category, and feature processing is very famous in face recognition.

3.Appearance-Based / Model-Based:-

The appearance-based method shows a face regarding several images. An image considered as a high dimensional vector. This technique is usually used to derive a feature space from the image division. The sample image compared to the training set. On the other hand, the model-based approach tries to model a face. The new sample implemented to the model and the parameters of the model used to recognise the image.

The appearance-based method can classify as linear or nonlinear. Ex- PCA, LDA, IDA used in direct approach whereas Kernel PCA used in nonlinear approach. On the other hand, in the model-based method can be classified as 2D or 3D Ex- Elastic Bunch Graph Matching used.

4.Template / Statistical / Neural Networks Based:-

4.1.Template Matching:-

In template matching the patterns are represented by samples, models, pixels, textures, etc. The recognition function is usually a correlation or distance measure.

4.2.Statistical Approach:-

In the Statistical approach, the patterns expressed as features. The recognition function in a discriminant function. Each image represented regarding d features. Therefore, the goal is to choose and apply the right statistical tool for extraction and analysis.

There are many statistical tools, which used for face recognition. These analytical tools used in a two or more groups or classification methods. These tools are as follows-

4.2.1.Principal Component Analysis [PCA]:-

One of the most used and cited statistical method is the Principal Component Analysis. A mathematical procedure performs a dimensionality reduction by extracting the principal component of multi-dimensional data.