Combining random forest with multi-block local binary pattern feature selection for multiclass head pose estimation

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.

Facial Expressions of Visitors in Forests along the Urbanization Gradient: What Can We Learn from Selfies on Social Networking Services?

“…This time period was also chosen to study check-in data of urban residents in Sina Micro-Blog [39]. Images containing the frontal view of an intact face were analyzed because this kind of image can best facilitate the computer recognition by the software [42]. Sometimes a head-pose in our collected images that resulted in non-frontal faces were still screened as candidates for expression analysis if all sensory organs were shown clearly at or near symmetry.…”

“…Sometimes a head-pose in our collected images that resulted in non-frontal faces were still screened as candidates for expression analysis if all sensory organs were shown clearly at or near symmetry. Although facial images with a range of head poses can be analyzed for emotional expression, the non-frontal-face images need to be screened to reduce unfavorable characteristics [42,43]. Therefore, this kind of image was excluded from our first screening.…”

Human Pose Recognition Based on Depth Image Multifeature Fusion

“…However, a single decision tree can easily lead to overfitting causing wrong classification. The random forest is composed of multiple decision trees [40,41], and the decision tree is trained with different data sets and parameters, which cannot only reduce the degree of overfitting but also the classification accuracy can be improved because its output is voted by multiple decision trees.…”

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

“…Multi-scale block local binary pattern (MB-LBP), a modified LBP operator, can extract texture information at different scales of an image and is not easily affected by image noise ( Liao et al, 2007 ; Zhang et al, 2007 ). It has been applied in studies on object detection and recognition ( Halidou et al, 2014 ; Li et al, 2015 ; Kang et al, 2017 ; Karanwal, 2021 ). To the best of our knowledge, there are no reports on the distinction between apple ring rot and apple anthracnose by using the image processing method based on LBP features.…”