This thesis proposes a new framework to generate a comprehensive artificial face detection database, termed Building a Comprehensive Face Detection Database from Face Recognition Databases, in which the challenges of face detection such as variation in poses, illumination, orientation, scale, background complexity, and image quality are considered as parameters. Instead of attempting to collect sample images as conventional approaches, we consider a framework where after selecting one or some available databases, so-called mother databases; facial regions are cropped according to facial contour using normalization coefficients. These cropped faces are adjusted according to users’ requirements to generate automatically a face detection database together with its facial ground truths. The framework is capable of addressing some existing issues of testing databases in evaluating face detection algorithms, such as: sufficiency of data, feasibility in generating algorithm-oriented face detection databases, reduction of consuming-time in locating ground truths.
In providing a sufficient data and generating algorithm-oriented face detection databases; we facilitate the controllability over the variations by establishing a formulation of leveling challenges of face detection. Therefore, the numbers of faces as well as those challenges which are amalgamated in the database and the range of the challenges are adjusted by users as input parameters. To reduce the time consuming in locating ground truths of faces in the database, the ground truths of cropped faces from the mother databases are marked manually. During the processing of adjusting the cropped faces in accordance with users’ requirements, the ground truths of the adjusted faces are computed automatically. Our novel approach marks a significant breakthrough in face detection evaluation since it facilitates the fair and comprehensive evaluation among algorithms.
To demonstrate the feasibility and effectives of our framework, the artificial face databases generated are compared to the commonly standard face detection database CMU-MIT. A highly generable rate up to over 91% is exposed. In addition, generating several face detection databases with pre-defined specifications is applied. These results illustrate the applicability of our framework.

This thesis proposes a new framework to generate a comprehensive artificial face detection database, termed Building a Comprehensive Face Detection Database from Face Recognition Databases, in which the challenges of face detection such as variation in poses, illumination, orientation, scale, background complexity, and image quality are considered as parameters. Instead of attempting to collect sample images as conventional approaches, we consider a framework where after selecting one or some available databases, so-called mother databases; facial regions are cropped according to facial contour using normalization coefficients. These cropped faces are adjusted according to users’ requirements to generate automatically a face detection database together with its facial ground truths. The framework is capable of addressing some existing issues of testing databases in evaluating face detection algorithms, such as: sufficiency of data, feasibility in generating algorithm-oriented face detection databases, reduction of consuming-time in locating ground truths.
In providing a sufficient data and generating algorithm-oriented face detection databases; we facilitate the controllability over the variations by establishing a formulation of leveling challenges of face detection. Therefore, the numbers of faces as well as those challenges which are amalgamated in the database and the range of the challenges are adjusted by users as input parameters. To reduce the time consuming in locating ground truths of faces in the database, the ground truths of cropped faces from the mother databases are marked manually. During the processing of adjusting the cropped faces in accordance with users’ requirements, the ground truths of the adjusted faces are computed automatically. Our novel approach marks a significant breakthrough in face detection evaluation since it facilitates the fair and comprehensive evaluation among algorithms.
To demonstrate the feasibility and effectives of our framework, the artificial face databases generated are compared to the commonly standard face detection database CMU-MIT. A highly generable rate up to over 91% is exposed. In addition, generating several face detection databases with pre-defined specifications is applied. These results illustrate the applicability of our framework.

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