Spartans: Single-Sample Periocular-Based Alignment-Robust Recognition Technique Applied to Non-Frontal Scenarios
Spartans: Single-Sample Periocular-Based Alignment-Robust Recognition Technique Applied to Non-Frontal Scenarios
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DOI：
10.1109/tip.2015.2468173
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发表时间：
2015-12-01
影响因子：
10.6
通讯作者：
Savvides, Marios
中科院分区：
文献类型：
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作者：
Juefei-Xu, Felix;Luu, Khoa;Savvides, Marios
在本文中，我们研究了一个单样本的基于眼周的鲁棒人脸识别技术，是姿态容忍下无约束的人脸匹配场景。我们的Spartans框架从每个主题类使用一个单一样本开始，并使用3D通用弹性模型在广泛的3D旋转下生成新的人脸图像，该模型既准确又计算经济。然后，我们重点关注保留人脸最稳定和最具鉴别力特征的眼周区域，并边缘化眼周区域以外的区域，因为它们更容易受到表情变化和遮挡的影响。一种新的面部描述，高维沃尔什局部二进制模式，均匀采样的人脸图像与鲁棒性对齐。在学习阶段，主题相关的先进的相关滤波器学习的姿态容忍的非线性子空间建模在核特征空间，其次是耦合的最大池机制，进一步提高性能。对于任何不受约束的未见过的人脸图像，斯巴达人可以产生一个高度区分的匹配分数，从而实现高验证率。我们已经在Wild数据库中具有挑战性的Labeled Faces上评估了我们的方法，并且在四个评估协议下以89.69%的高准确率远远超过了最先进的算法，这是图像限制和无监督协议中的最高分。Spartans的进步也在人脸识别大挑战和Multi-PIE数据库中得到了证明。此外，我们的学习方法的基础上先进的相关滤波器是更有效的，在学习主题相关的姿态容忍的子空间，与许多成熟的子空间方法相比，在线性和非线性的情况下。
In this paper, we investigate a single-sample periocular-based alignment-robust face recognition technique that is pose-tolerant under unconstrained face matching scenarios. Our Spartans framework starts by utilizing one single sample per subject class, and generate new face images under a wide range of 3D rotations using the 3D generic elastic model which is both accurate and computationally economic. Then, we focus on the periocular region where the most stable and discriminant features on human faces are retained, and marginalize out the regions beyond the periocular region since they are more susceptible to expression variations and occlusions. A novel facial descriptor, high-dimensional Walsh local binary patterns, is uniformly sampled on facial images with robustness toward alignment. During the learning stage, subject-dependent advanced correlation filters are learned for pose-tolerant non-linear subspace modeling in kernel feature space followed by a coupled max-pooling mechanism which further improve the performance. Given any unconstrained unseen face image, the Spartans can produce a highly discriminative matching score, thus achieving high verification rate. We have evaluated our method on the challenging Labeled Faces in the Wild database and solidly outperformed the state-of-the-art algorithms under four evaluation protocols with a high accuracy of 89.69%, a top score among image-restricted and unsupervised protocols. The advancement of Spartans is also proven in the Face Recognition Grand Challenge and Multi-PIE databases. In addition, our learning method based on advanced correlation filters is much more effective, in terms of learning subject-dependent pose-tolerant subspaces, compared with many well-established subspace methods in both linear and non-linear cases.