A binary descriptor typically consists of three stages: image filtering, binarization, and spatial histogram. This paper first demonstrates that the binary code of the maximum-variance filtering responses leads to the lowest bit error rate under Gaussian noise. Then, an optimal eigenfilter bank is derived from a universal assumption on the local stationary random field. Finally, compressive binary patterns (CBP) is designed by replacing the local derivative filters of local binary patterns (LBP) with these novel random-field eigenfilters, which leads to a compact and robust binary descriptor that characterizes the most stable local structures that are resistant to image noise and degradation. A scattering-like operator is subsequently applied to enhance the distinctiveness of the descriptor. Surprisingly, the results obtained from experiments on the FERET, LFW, and PaSC databases show that the scattering CBP (SCBP) descriptor, which is handcrafted by only 6 optimal eigenfilters under restrictive assumptions, outperforms the state-of-the-art learning-based face descriptors in terms of both matching accuracy and robustness. In particular, on probe images degraded with noise, blur, JPEG compression, and reduced resolution, SCBP outperforms other descriptors by a greater than 10 percent accuracy margin.

中文翻译：

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压缩二进制模式：使用随机场特征滤波器设计鲁棒的二进制面部描述符


二进制描述符通常由三个阶段组成：图像滤波、二值化和空间直方图。本文首先证明了最大方差滤波响应的二进制码在高斯噪声下具有最低的误码率。然后，根据局部平稳随机场的通用假设导出最优特征滤波器组。最后，通过用这些新颖的随机场特征滤波器替换局部二进制模式（LBP）的局部导数滤波器来设计压缩二进制模式（CBP），从而产生紧凑且鲁棒的二进制描述符，该描述符描述了最稳定的抗性局部结构图像噪声和退化。随后应用类散射算子来增强描述符的独特性。令人惊讶的是，从 FERET、LFW 和 PaSC 数据库上的实验获得的结果表明，在限制性假设下仅由 6 个最佳特征滤波器手工制作的散射 CBP (SCBP) 描述符优于最先进的基于学习的描述符。人脸描述符的匹配精度和鲁棒性。特别是，在因噪声、模糊、JPEG 压缩和分辨率降低而降级的探测图像上，SCBP 的准确度比其他描述符高出 10% 以上。