This paper aims to examine the effects of aging on the efficiency of facial recognition algorithms in terms of age groups and age difference intervals. A comparative analysis of the recognition performance of two approaches is conducted for different age groups and different length time intervals between images. The first approach uses a two-dimensional discrete cosine transform (2D-DCT) and a Kernel Fisher Analysis (KFA) as description tools; classification is made using a k-NN classifier based on Euclidean distance. However, the second one is performed in two ways: first, we considered face as a single entity, then we viewed face as an independent component set. This approach makes use of Convolutional Neural Networks (CNN) for description and Support vector machines (SVM) for classification. Achieved results using the publicly accessible FG-NET face database prove that age groups influence the performance of face recognition algorithms. Also, time length lapses between images can significantly reduce the performance of face recognition.

This paper presents a teaching-learning-based optimization algorithm for discrete large-scale multi-objective problems (DLM-TLBO). Unlike the previous variants, the learning strategy used by each individual and the acquired knowledge are defined based on its level. The proposed approach is used to solve a bi-objective object clustering task (B-OCT) in a swarm robotic system, as a case study. The simple robots have as mission the gathering of a number of objects distributed randomly, while respecting two objectives: maximizing the clustering quality, and minimizing the energy consumed by these robots. The simulation results of the proposed algorithm are compared to those obtained by the well-known algorithm NSGA-II. The results show the superiority of the proposed DLM-TLBO in terms of the quality of the obtained Pareto front approximation and convergence speed.

In this paper, we have proposed a novel feature-based fragile watermarking scheme for image authentication. The proposed technique extracts Feature Points (FP) by performing the Harris corner detector and used them as germs to decomposes the host image in segments using Voronoi Diagram (VD). The authentication of each segment is guaranteed by using the Cyclic Redundancy Check code (CRC). Then, the CRC encoding procedure is applied to each segment to generate the watermark. Voronoi decomposition is employed because it has a good retrieval performance compared to similar geometrical decomposition algorithms. The security aspect of our proposed method is achieved by using the public key crypto-system RSA (Rivest–Shamir–Adleman) to encrypt the FP. Experimental results demonstrate the efficiency of our approach in terms of imperceptibility, the capability of detection of alterations, the capacity of embedding, and computation time. We have also prove the impact of VD decomposition on the quality of the watermarked image compared to block decomposition. The proposed method can be applicable in the case where the tamper detection is critical and only some regions of interest must be re-transmitted if they are corrupted, like in the case of medical images. An example of the application of our approach to medical image is briefly presented.