A Line Matching Method Based on Multiple Intensity Ordering with Uniformly Spaced Sampling

This paper presents a line matching method based on multiple intensity ordering with uniformly spaced sampling. Line segments are extracted from the image pyramid, with the aim of adapting scale changes and addressing fragmentation problem. The neighborhood of line segments was divided into sub-regions adaptively according to intensity order to overcome the difficulty brought by various line lengths. An intensity-based local feature descriptor was introduced by constructing multiple concentric ring-shaped structures. The dimension of the descriptor was reduced significantly by uniformly spaced sampling and dividing sample points into several point sets while improving the discriminability. The performance of the proposed method was tested on public datasets which cover various scenarios and compared with another two well-known line matching algorithms. The experimental results show that our method achieves superior performance dealing with various image deformations, especially scale changes and large illumination changes, and provides much more reliable correspondences.

PointNet++ and Three Layers of Features Fusion for Occlusion Three-Dimensional Ear Recognition Based on One Sample per Person

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.