Face Recognition Performance in Facing Pose Variation
Abstract: There are many real
world applications of face recognition which require good performance in
uncontrolled environments such as social networking, and environment
surveillance. However, many researches of face recognition are done in
controlled situations. Compared to the controlled environments, face
recognition in uncontrolled environments comprise more variation, for example
in the pose, light intensity, and expression. Therefore, face recognition in
uncontrolled conditions is more challenging than in controlled settings. In
this research, we would like to discuss handling pose variations in face
recognition. We address the representation issue us ing multi-pose of face
detection based on yaw angle movement of the head as extensions of the existing
frontal face recognition by using Principal Component Analysis (PCA). Then, the
matching issue is solved by using Euclidean distance. This combination is known
as Eigenfaces method. The experiment is done with different yaw angles and
different threshold values to get the optimal results. The experimental results
show that: (i) the more pose variation of face images used as training data is,
the better recognition results are, but it also increases the processing time,
and (ii) the lower threshold value is, the harder it recognizes a face image,
but it also increases the accuracy.
Author: Alexander Agung
Santoso Gunawan, Reza A Prasetyo
Journal Code: jptinformatikagg170006
