Masked Face Recognition via CNN Embeddings Optimized with aDiscriminative Quadruplet Loss Function - Ahmam, Siham; Safiny, Yazid; Lamghari, Nidal; Ghazdali, Abdelghani (Informatica, 2026)
Masked face recognition remains a challenging problem because masks occlude key facial regions that arecrucial for identity verification. In this paper, we propose a deep convolutional architecture composed ofstacked Conv–BatchNorm–MaxPooling blocks followed by dropout, a flatten layer, and a dense embeddinglayer trained with an improved quadruplet loss. Each quadruplet consists of two images from thesame identity and two from different identities, enforcing compact intra-class clusters and well-separatedinter-class distributions in the embedding space. We investigate three similarity measures on the learnedembeddings: Euclidean distance, Manhattan distance, and a learned similarity network. The best performanceis obtained with Euclidean distance: on our ENSA-MFRD dataset of 32,186 masked face imagescollected from university students, the proposed model reaches an accuracy of 99.27%, outperforming thestandard triplet loss and the original quadruplet loss by 0.62% and 0.72%, respectively. Using the learnedsimilarity network, our approach also surpasses the triplet and original quadruplet losses by 37.5% and11.87%, respectively. The model is further evaluated on four public benchmarks—COMASK20, MFRD-80K, CASIA-WebMaskedFace, and LFW-SMFD—where it consistently improves accuracy, F1-score overboth baselines. These results demonstrate that the proposed architecture and enhanced quadruplet lossyield robust and discriminative representations for masked face recognition across diverse datasets andacquisition conditions.