论文标题
通过多种等价关系学习描述符的不变性：一种表达3D面部识别的新方法
Learning Descriptors Invariance Through Equivalence Relations Within Manifold: A New Approach to Expression Invariant 3D Face Recognition
论文作者
论文摘要
本文为关键点描述符的有用和不良变化之间的二分法提供了一种独特的方法，即描述符（特征）空间中的身份和表达变化。描述符的变体是从培训示例中学到的。根据培训数据的标签，建立了描述符之间的等效关系。两种类型的描述符变异都由描述符歧管中嵌入的图表示。然后将不变识别作为图形搜索问题进行。设计了适合此设置下识别的启发式图搜索算法。提出的方法是对FRGC V2.0，Bosphorus和3D TEC数据集的测试。它已证明可以通过相当大的边缘提高识别性能，尤其是在表达变化下。
This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on the labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. The invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tests on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations in particular, by considerable margins.
