Occlusion over ear surfaces results in performance degradation of ear registration and recognition systems. In this paper, we propose an occlusion-resistant three-dimensional (3D) ear recognition system consisting of four primary components: (1) an ear detection component, (2) a local feature extraction and matching component, (3) a holistic matching component, and (4) a decision-level fusion algorithm. The ear detection component is implemented based on faster region-based convolutional neural networks. In the local feature extraction and matching component, a symmetric space-centered 3D shape descriptor based on the surface patch histogram of indexed shapes (SPHIS) is used to generate a set of keypoints and a feature vector for each keypoint. Then, a two-step noncooperative game theory (NGT)-based method is proposed. The proposed symmetric game-based method is effectively applied to determine a set of keypoints that satisfy the rigid constraints from initial keypoint correspondences. In the holistic matching component, a proposed variant of breed surface voxelization is used to calculate the holistic registration error. Finally, the decision-level fusion algorithm is applied to generate the final match scores. Evaluation results from experiments conducted show that the proposed method produces competitive results for partial occlusion on a dataset consisting of natural and random occlusion.
The ear’s relatively stable structure makes it suitable for recognition. In common identification applications, only one sample per person (OSPP) is registered in a gallery; consequently, effectively training deep-learning-based ear recognition approach is difficult. The state-of-the-art (SOA) 3D ear recognition using the OSPP approach bottlenecks when large occluding objects are close to the ear. Hence, we propose a system that combines PointNet++ and three layers of features that are capable of extracting rich identification information from a 3D ear. Our goal is to correctly recognize a 3D ear affected by a large nearby occlusion using one sample per person (OSPP) registered in a gallery. The system comprises four primary components: (1) segmentation; (2) local and local joint structural (LJS) feature extraction; (3) holistic feature extraction; and (4) fusion. We use PointNet++ for ear segmentation. For local and LJS feature extraction, we propose an LJS feature descriptor–pairwise surface patch cropped using a symmetrical hemisphere cut-structured histogram with an indexed shape (PSPHIS) descriptor. Furthermore, we propose a local and LJS matching engine based on the proposed LJS feature descriptor and SOA surface patch histogram indexed shape (SPHIS) local feature descriptor. For holistic feature extraction, we use a voxelization method for global matching. For the fusion component, we use a weighted fusion method to recognize the 3D ear. The experimental results demonstrate that the proposed system outperforms the SOA normalization-free 3D ear recognition methods using OSPP when the ear surface is influenced by a large nearby occlusion.
When only one sample per person (OSPP) is registered in the gallery, it is difficult for ear recognition methods to sufficiently and effectively reduce the search range of the matching features, thus resulting in low computational efficiency and mismatch problems. A 3D ear biometric system using OSPP is proposed to solve this problem. By categorising ear images by shape and establishing the corresponding relationship between keypoints from ear images and regions (regional cluster) on the directional proposals that can be arranged to roughly face the ear image, the corresponding keypoints are obtained. Then, ear recognition is performed by combining corresponding keypoints and a multikeypoint descriptor sparse representation classification method. The experimental results conducted on the University of Notre Dame Collection J2 dataset yielded a rank-1 recognition rate of 98.84%; furthermore, the time for one identification operation shared by each gallery subject was 0.047 ms.
To explore the effect of associated bacteria on the low-temperature adaptability of pinewood nematodes (PWNs), transcriptome sequencing (RNA-seq) of PWN AH23 treated with the associated bacterial strain Bacillus cereus GD1 was carried out with reference to the whole PWN genome. Bioinformatic software was utilized to analyze the differentially expressed genes (DEGs). This study was based on the analysis of DEGs to verify the function of daf-11 by RNAi. The results showed that there were 439 DEGs between AH23 treated with GD1 and those treated with ddH2O at 10 °C. There were 207 pathways annotated in the KEGG database and 48 terms annotated in the GO database. It was found that after RNAi of daf-11, the survival rate of PWNs decreased significantly at 10 °C, and fecundity decreased significantly at 15 °C. It can be concluded that the associated bacteria GD1 can enhance the expression of genes related to PWN low-temperature adaptation and improve their adaptability to low temperatures.
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