Abstract
A new head pose estimation technique based on Random Forest (RF) and texture features for facial image analysis using a monocular camera is proposed in this paper, especially about how to efficiently combine the random forest and the features. In the proposed technique a randomized tree with useful attributes is trained to improve estimation accuracy and tolerance of occlusions and illumination. Specifically, a number of features including Multi-scale Block Local Block Pattern (MB-LBP) are extracted from an image, and random features such as the MB-LBP scale parameters, a block coordinate, and a layer of an image pyramid in the feature pool are used for training the tree. The randomized tree aims to maximize the information gain at each node while random samples traverse the nodes in the tree. To this aim, a split function considering the uniform property of the LBP feature is developed to move sample blocks to the left or the right children nodes. The trees are independently trained with random inputs, yet they are grouped to form a random forest so that the results collected from the trees are used for make the final decision. Precisely, we use a Maximum-A-Posteriori criterion in the decision. It is demonstrated with experimental results that the proposed technique provides significantly enhanced classification performance in the head pose estimation in various conditions of illumination, poses, expressions, and facial occlusions.
Highlights
Head pose estimation is the front-end technique to infer the changes in view points of a human face in an image as the heading estimation is important in human navigation and locomotion [1, 2]
It is noted that any pre-processing technique to resolve the lightening variation is not applied to clearly show the performance of the proposed technique
We use an alignment algorithm for the Local Binary Pattern (LBP)-based descriptors to cope with geometric invariance
Summary
Head pose estimation is the front-end technique to infer the changes in view points of a human face in an image as the heading estimation is important in human navigation and locomotion [1, 2]. As compared to the works, the proposed technique shows how to combine the random Forest with efficient facial analysis features for the head pose estimation. We propose the multiclass head pose estimation algorithm at the coarse-level prediction, which uses a randomized tree incorporating an multi-block LBP (MB-LBP) to be reliable with facial occlusions. We develop the randomized tree that includes an effective split function to learn important facial patterns represented by the LBP descriptors. The trees grouped in the random forest are used for the final decision by using Maximum-A-Posteriori (MAP) criterion It is demonstrated in the experimental results that the integration of the developed features and the random forest achieves significantly improved classification performance in various conditions of illumination, poses, expressions, and facial occlusions
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