Face sketch-photo synthesis can be regarded as an image-to-image translation problem. Although many generative models achieve good translations from sketches to photos, they still have limitations in preserving face identity due to the huge modality gap of the two domains. To this end, we propose an identity-preserved adversarial model (IPAM), which includes an extended U-Net to increase the weight of the original sketch in translation, two discriminators focusing on the real or fake image concatenation of two domains to learn more styles of the target domain, and an identity constraint to request the fakes and the real targets to have zero cosine distance in feature space. We evaluate our method on two face sketch databases with face recognition. The results demonstrate our translation method is superior to the existing methods in maintaining face identity information.

Heterogeneous face recognition (HFR), referring to matching face images across different domains, is a challenging problem due to the vast cross-domain discrepancy and insufficient pairwise cross-domain training data. This paper proposes a quadruplet framework for learning domain-invariant discriminative features (DIDF) for HFR, which integrates domain-level and class-level alignment in one unified network. The domain-level alignment reduces the cross-domain distribution discrepancy. The class-level alignment based on a special quadruplet loss is developed to further diminish the intra-class variations and enlarge the inter-class separability among instances, thus handling the misalignment and adversarial equilibrium problems confronted by the domain-level alignment. With a bidirectional cross-domain data selection strategy, the quadruplet loss-based method prominently enriches the training set and further eliminates the cross-modality shift. Benefiting from the joint supervision and mutual reinforcement of these two components, the domain invariance and class discrimination of identity features are guaranteed. Extensive experiments on the challenging CASIA NIR-VIS 2.0 database, the Oulu-CASIA NIR&VIS database, the BUAA-VisNir database, and the IIIT-D viewed sketch database demonstrate the effectiveness and preferable generalization capability of the proposed method.

Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization problems over continuous space and has been widely used in many fields of science and engineering. How to avoid the local optimal solution and how to improve the convergence performance of DE are hotpot problems for many researchers. In this paper, an improved differential evolution algorithm based on dual-strategy (DSIDE) is proposed. The DSIDE algorithm has two strategies. (1) An enhanced mutation strategy based on “DE/rand/1,” which takes into account the influence of reference individuals on mutation and has strong global exploration and convergence ability. (2) A novel adaptive strategy for scaling factor and crossover probability based on fitness value has a positive impact on population diversity. The DSIDE algorithm is verified with other seven state-of-the-art DE variants under 30 benchmark functions. Furthermore, Wilcoxon sign rank-sum test, Friedman test, and Kruskal–Wallis test are utilized to analyze the results. The experiment results show that the proposed DSIDE algorithm can significantly improve the global optimization performance.