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：设计具有随机场特征滤波器的鲁棒二进制脸部描述符

二进制描述符通常包括三个阶段：图像过滤，二值化和空间直方图。本文首先证明，在高斯噪声下，最大方差滤波响应的二进制代码导致最低的误码率。然后，从局部平稳随机场的通用假设中得出最佳本征滤波器组。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），这导致了紧凑而健壮的二进制描述符，该描述符描述了具有抗性的最稳定的局部结构成像噪声和降级。随后应用类似散射的算子来增强描述符的独特性。出奇，从FERET，LFW和PaSC数据库上进行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）描述符的性能优于基于学习的最新人脸描述符在匹配精度和鲁棒性方面。特别是，在因噪声，模糊，JPEG压缩和降低的分辨率而降级的探针图像上，SCBP的准确性优于其他描述符，超过10％。