Abstract
Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.
Highlights
Face recognition has been the subject of research for many years and has been used in countless applications in many different areas
The framework describes the complete process of principal component analysis (PCA)-based face recognition, and in each step, multiple variations are offered for different requirements
There exists a number of image processing toolkits like OpenCV, which have PCA algorithm as well as associated approaches for face recognition, it is still time-consuming for software developers who intend to integrate face recognition implementations with their own applications
Summary
Face recognition has been the subject of research for many years and has been used in countless applications in many different areas. It presents a unique high-level design framework for face recognition application development that allows a general design to be customized to produce specific applications based on selected design variations It presents the implementation of the framework, which helps developers when choosing a suitable approach for each step of the PCA-based face recognition development process by fostering component reuse. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. There exists a number of image processing toolkits like OpenCV, which have PCA algorithm as well as associated approaches for face recognition, it is still time-consuming for software developers who intend to integrate face recognition implementations with their own applications.
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