The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

@article{Soon2018,
  title     = {The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {12},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Tioh Keat Soon and Abd Samad Hasan Basari and Nuzulha Khilwani Ibrahim and Burairah Hussin and Ariff Idris and Noorayisahbe Mohd Yaacob and Mustafa Almahdi Algaet and Norazira A. Jalil},
  doi       = {10.14569/IJACSA.2018.091211},
  url       = {https://doi.org/10.14569/IJACSA.2018.091211}
}