Kinship verification from face images is a novel and formidable challenge in
the realms of pattern recognition and computer vision. This work makes notable
contributions by incorporating a preprocessing technique known as Multiscale
Retinex (MSR), which enhances image quality. Our approach harnesses the
strength of complementary deep (VGG16) and shallow texture descriptors (BSIF)
by combining them at the score level using Logistic Regression (LR) technique.
We assess the effectiveness of our approach by conducting comprehensive
experiments on three challenging kinship datasets: Cornell Kin Face, UB Kin
Face and TS Kin Face

本文通过 Multiscale Retinex 技术提升图像质量，并结合深度和浅层纹理描述符通过逻辑回归方法在得分级别上进行联合，实现了面部图像的亲属关系验证。作者通过对 Cornell Kin Face、UB Kin Face 和 TS Kin Face 等三个具有挑战性的亲属关系数据集进行了全面的实验，评估了该方法的有效性。

融合深度和浅层特征用于人脸亲属关系验证

Fusion of Deep and Shallow Features for Face Kinship Verification

The challenge of kinship verification from facial images represents a
cutting-edge and formidable frontier in the realms of pattern recognition and
computer vision. This area of study holds a myriad of potential applications,
spanning from image annotation and forensic analysis to social media research.
Our research stands out by integrating a preprocessing method named Multiscale
Retinex (MSR), which elevates image quality and amplifies contrast, ultimately
bolstering the end results. Strategically, our methodology capitalizes on the
harmonious blend of deep and shallow texture descriptors, merging them
proficiently at the score level through the Logistic Regression (LR) method. To
elucidate, we employ the Local Phase Quantization (LPQ) descriptor to extract
shallow texture characteristics. For deep feature extraction, we turn to the
prowess of the VGG16 model, which is pre-trained on a convolutional neural
network (CNN). The robustness and efficacy of our method have been put to the
test through meticulous experiments on three rigorous kinship datasets, namely:
Cornell Kin Face, UB Kin Face, and TS Kin Face.

运用多尺度 Retinex 前处理方法，结合深浅纹理描述符，通过逻辑回归方法在评分水平上巧妙地融合，从而提高图像质量和对比度，从而在亲缘验证方面取得了鲁棒性和效果优异的结果。