3D detection of vehicles is an essential component for autonomous driving applications. Nevertheless, collecting the supervised training data for learning 3D vehicle detectors would be costly (e.g. utilization of expensive LiDAR sensors) and labor-intensive (for human annotation). In comparison to 3D detection, 2D object detection has achieved a welldeveloped status, boosting stable and robust performance with widespread application in numerous fields, thanks to the large scale (i.e. amount of samples) of existing training datasets of 2D object detection. Hence, in our work, we propose to realize 3D detection via leveraging the robustness of 2D detectors and developing a network that lifts 2D detections to 3D. With the flexibility of building upon various backbone models (e.g. the models which take image regions detected by 2D detector as inputs to predict their corresponding 3D bounding boxes, or the existing monocular 3D detection models which have the intermediate output of 2 D bounding boxes), we propose several geometry-driven objectives, including projection consistency loss, geometry depth loss, and opposite bin loss, to improve the training upon 2D-to-3D lifting. Our extensive experimental results demonstrate that our proposed geometrydriven objectives not only contribute to the superior results of 3D detection but also provide better generalizability across datasets.

Ocular recognition is expected to provide a higher flexibility in handling practical applications as oppose to the iris recognition, which only works for the ideal open-eye case. However, the accuracy of the recent efforts is still far from satisfactory at uncontrollable conditions, such as eye blinking which implies any poses of eyes. To address these issues, the skin texture, eyelids, and additional geometrical features are employed. In addition, to achieve higher accuracy, sequential forward floating selection is utilized to select the best feature combinations. Finally, the non-linear support vector machine is applied for identification purpose. Experimental results demonstrate that the proposed algorithm achieves the best accuracy for both open eye and blinking eye scenarios. As a result, it offers greater flexibility for the prospective subjects during recognition as well as higher reliability for security.

Facial landmark extraction system is crucial in various applications, including face recognition, expression analysis, face tracking, and face animation. This letter aims to improve the performance of an existing landmark extraction method proposed by Ren et al. in terms of error rate. Specifically, the Gaussian blur filter is applied on the input image to reduce noise interference and the theta-based split rule is deployed to strengthen the performance of the random forests. Then, global linear regression is applied instead of treating each landmark independently. Experimental results demonstrate that the proposed modified facial landmark extraction algorithm outperforms the conventional methods for both the LFPW and Helen databases.