Great progress has been achieved in face recognition in the last three decades. However, it is still challenging to characterize the identity related features in face images. This paper proposes a novel facial feature extraction method named Gabor ordinal measures (GOM), which integrates the distinctiveness of Gabor features and the robustness of ordinal measures as a promising solution to jointly handle inter-person similarity and intra-person variations in face images. In the proposal, different kinds of ordinal measures are derived from magnitude, phase, real, and imaginary components of Gabor images, respectively, and then are jointly encoded as visual primitives in local regions. The statistical distributions of these visual primitives in face image blocks are concatenated into a feature vector and linear discriminant analysis is further used to obtain a compact and discriminative feature representation. Finally, a two-stage cascade learning method and a greedy block selection method are used to train a strong classifier for face recognition. Extensive experiments on publicly available face image databases, such as FERET, AR, and large scale FRGC v2.0, demonstrate state-of-the-art face recognition performance of GOM.

It has been shown in different studies the benefits of using spatio-temporal information for video face recognition. However, most of the existing spatio-temporal representations do not capture the local discriminative information present in human faces. In this paper we introduce a new local spatio-temporal descriptor, based on structured ordinal features, for video face recognition. The proposed method not only encodes jointly the local spatial and temporal information, but also extracts the most discriminative facial dynamic information while trying to discard spatio-temporal features related to intra-personal variations. Besides, a similarity measure based on a set of background samples is proposed to be used with our descriptor, showing to boost its performance. Extensive experiments conducted on the recent but difficult YouTube Faces database demonstrate the good performance of our proposal, achieving state-of-the-art results.

Given the current COVID-19 pandemic, most people wear a mask to effectively prevent the spread of the contagious disease. This sanitary measure has caused a significant drop in the effectiveness of current face recognition methods when handling masked faces on practical applications such as face access control, face attendance, and face authentication-based mobile payment. Under this situation, recent efforts have been focused on boosting the performance of the existing face recognition technology on masked faces. Some solutions trying to tackle this issue fine-tune the existing deep learning face recognition models on synthetic masked images, while others use the periocular region as a naive manner to eliminate the adverse effect of COVID-19 masks. Although the accuracy of masked face recognition remains an important issue, in the last few years, the development of efficient and lightweight face recognition methods has received an increased attention in the research community. In this paper, we study the effectiveness of three state-of-the-art lightweight face recognition models for addressing accurate and efficient masked face recognition, considering both fine-tuning on masked faces and periocular images. For the experimental evaluation, we create both real and simulated masked face databases as well as periocular datasets. Extensive experiments are conducted to determine the most effective solution and state further steps for the research community. The obtained results disclose that fine-tuning existing state-of-the-art face models on masked images achieve better performance than using periocular-based models. Besides, we evaluate and analyze the effectiveness of the trained masked-based models on well-established unmasked benchmarks for face recognition and assess the efficiency of the used lightweight architectures in comparison with state-of-the-art face models.