MediaTek Inc., Hsinchu 30078, Taiwan.
IEEE Trans Image Process. 2012 Sep;21(9):4280-9. doi: 10.1109/TIP.2012.2202670. Epub 2012 Jun 5.
Illumination compensation and normalization play a crucial role in face recognition. The existing algorithms either compensated low-frequency illumination, or captured high-frequency edges. However, the orientations of edges were not well exploited. In this paper, we propose the orientated local histogram equalization (OLHE) in brief, which compensates illumination while encoding rich information on the edge orientations. We claim that edge orientation is useful for face recognition. Three OLHE feature combination schemes were proposed for face recognition: 1) encoded most edge orientations; 2) more compact with good edge-preserving capability; and 3) performed exceptionally well when extreme lighting conditions occurred. The proposed algorithm yielded state-of-the-art performance on AR, CMU PIE, and extended Yale B using standard protocols. We further evaluated the average performance of the proposed algorithm when the images lighted differently were observed, and the proposed algorithm yielded the promising results.
光照补偿和归一化在人脸识别中起着至关重要的作用。现有的算法要么补偿低频光照，要么捕捉高频边缘。然而，边缘的方向并没有得到很好的利用。在本文中，我们简要地提出了有向局部直方图均衡化（OLHE），它在补偿光照的同时，对边缘方向的丰富信息进行编码。我们声称边缘方向对于人脸识别是有用的。我们提出了三种 OLHE 特征组合方案用于人脸识别：1）编码最多的边缘方向；2）更紧凑且具有良好的边缘保持能力；3）在极端光照条件下表现异常出色。使用标准协议，所提出的算法在 AR、CMU PIE 和扩展 Yale B 上取得了最先进的性能。我们进一步评估了在观察不同光照的图像时，所提出算法的平均性能，所提出的算法产生了有希望的结果。
