3-D Face Recognition Using Curvelet Local Features
3-D Face Recognition Using Curvelet Local Features
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DOI：
10.1109/lsp.2013.2295119
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发表时间：
2014-02-01
影响因子：
3.9
通讯作者：
El-Sallam, A.
中科院分区：
文献类型：
--
作者：
Elaiwat, S.;Bennamoun, M.;El-Sallam, A.
在这封信中，我们提出了一个强大的单模态特征为基础的3-D人脸识别算法。该算法利用Curvelet变换不仅可以检测人脸上的显著点，还可以构建多尺度局部表面描述符，该描述符可以捕获检测到的关键点周围的高度独特的旋转/位移不变局部特征。这种方法被证明是在不同的光照条件和面部表情下提供鲁棒和准确的识别。使用众所周知的和具有挑战性的FRGC v2数据集，我们报告了一个上级性能相比，其他算法，与97.83%的验证率与所有面部表情的探针。
In this letter, we present a robust single modality feature-based algorithm for 3-D face recognition. The proposed algorithm exploits Curvelet transform not only to detect salient points on the face but also to build multi-scale local surface descriptors that can capture highly distinctive rotation/displacement invariant local features around the detected keypoints. This approach is shown to provide robust and accurate recognition under varying illumination conditions and facial expressions. Using the well-known and challenging FRGC v2 dataset, we report a superior performance compared to other algorithms, with a 97.83% verification rate for probes with all facial expressions.