TY  - JOUR
T1  - Face Detection using Orientation(In-Plane Rotation) Invariant Facial Region Segmentation and Local Binary Patterns(LBP)
AU  - Lee, Hee-Jae 
AU  - Kim, Ha-Young 
AU  - Lee, David 
AU  - Lee, Sang-Goog 
JO  - Journal of KIISE, JOK
PY  - 2017
DA  - 2017/1/14
DO  - 10.5626/JOK.2017.44.7.692
KW  - face detection
KW  - facial region segmentation
KW  - orientation(in-plane rotation) invariant
KW  - local binary patterns
KW  - structural similarity index
AB  - Face detection using the LBP based feature descriptor has issues in that it can not represent spatial information between facial shape and facial components such as eyes, nose and mouth. To address these issues, in previous research, a facial image was divided into a number of square sub-regions. However, since the sub-regions are divided into different numbers and sizes, the division criteria of the sub-region suitable for the database used in the experiment is ambiguous, the dimension of the LBP histogram increases in proportion to the number of sub-regions and as the number of sub-regions increases, the sensitivity to facial orientation rotation increases significantly. In this paper, we present a novel facial region segmentation method that can solve in-plane rotation issues associated with LBP based feature descriptors and the number of dimensions of feature descriptors. As a result, the proposed method showed detection accuracy of 99.0278% from a single facial image rotated in orientation.