Enhancing Kinship Verification through Multiscale Retinex and Combined
  Deep-Shallow features
El Ouanas Belabbaci Mohammed Khammari Ammar Chouchane Mohcene Bessaoudi Abdelmalik Ouamane Yassine Himeur Shadi Atalla Wathiq Mansoor
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.