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The problem of identifying and analyzing faces in images is a fundamental task in computer vision. Though great progress has been achieved in face detection, it is still difficult to obtain the pose estimation. In this paper we propose a pose estimation approach that is based on time series representation. We have converted input images of faces into big time series datasets, and we then used a dimensionality reduction method to convert the original series to a symbolic representation. Classification algorithms are then applied using the distances between the symbolic sequences of time series. Since external conditions when capturing images are not always optimal, pose estimation can become a challenge. In order to overcome such problems, we propose to use the gradient image and the Local Binary Pattern (LBP) combined with dynamic morphological quotient image (DMQI-LBP), where these descriptors are robust to changes in illumination. Classification algorithms such as K-means, SVM and KNN were evaluated to classify frontal vs profile faces poses, and the obtained experimental results show that the proposed method is very efficient.

Local Patterns and Big Time Series Data for Facial Poses Classification