The ability of humans to infer head poses from face shapes, and vice versa, indicates a strong correlation between them. Recent studies on face alignment used head pose information to predict facial landmarks in computer vision tasks. However, many studies have been limited to using head pose information primarily to initialize mean landmarks, as it cannot represent detailed face shapes. To enhance face alignment performance through effective utilization, we introduce a novel approach that integrates head pose information into the feature maps of a face alignment network, rather than simply using it to initialize facial landmarks. Furthermore, the proposed network structure achieves reliable face alignment through a dual-dimensional network. This structure uses multidimensional features such as 2D feature maps and a 3D heatmap to reduce reliance on a single type of feature map and enrich the feature information. We also propose a dense face alignment method through an appended fully connected layer at the end of a dual-dimensional network, trained with sparse face alignment. This method easily trains dense face alignment by directly using predicted keypoints as knowledge and indirectly using semantic information. We experimentally assessed the correlation between the predicted facial landmarks and head pose information, as well as variations in the accuracy of facial landmarks with respect to the quality of head pose information. In addition, we demonstrated the effectiveness of the proposed method through a competitive performance comparison with state-of-the-art methods on the AFLW2000-3D, AFLW, and BIWI datasets. In the evaluation of the face alignment task, we achieved an NME of 3.21 for the AFLW2000-3D and 3.68 for the AFLW dataset.

The mean‐shift method is widely used to locate a target object quickly in sequential images. The mean‐shift algorithm takes advantage of a colour distribution with a uniform quantisation. However, the quantisation method ignores the close relationship of colour statistics. The uniform distribution also results in a colour histogram with many empty bins, which introduces additional computation cost in the tracking procedure. To reduce the number of these redundant, empty bins, the authors present a new optimal colour‐based, mean‐shift algorithm for tracking objects. In the proposed method, the optimal colours are extracted by a histogram agglomeration, which clusters three‐dimensional (3D) colour histogram bins with the frequency ratios of 3D colour values. After obtaining optimal colours in a RGB colour histogram, the target image is represented by the indices of the optimal colours. The mean‐shift algorithm thus creates a confidence map in a candidate image based on the optimal colour histogram in the target image. It then finds the peak of the confidence map near the previous position of an object area. Comparative experiments with the conventional mean‐shift method showed that our method has the advantages of decreased processing time and improved tracking accuracy.

Ultrasonic sensors have been widely used in recognizing the working environment for a mobile robot. However, their intrinsic problems, such as the specular reflection, the wide beam angle, and the slow propagation velocity, require an excessive number of sensors to be integrated to achieve the various sensing goals. This paper proposes new measurement scheme which uses only two sets of ultrasonic sensors to determine the location and the type of target surface. By measuring the time difference between the returned signals from the target surface, which are generated by two transmitters with 1 ms difference, it classifies type and determines the pose of the target surface. Since the proposed sensor system uses only the two sets of ultrasonic sensors to recognize and localize the target surface, it significantly simplifies the sensing system and reduces the signal processing time so that the working environment can be recognized in real time.