Eyeglass replacement system is presented in this work. The proposed system automatically replaces the eyeglass frame from an input frontal face image with another one. First, the system generates a mask that specifies the location of the eyeglass frame in the input image. Next, the unwanted pixels from the image are completed using patch-based synthesis. Finally, the user chooses a new eyeglass frame from a dataset of eyeglass frames to see his/her frontal image with the selected one. One of useful applications of the proposed system is an electronic eyeglasses shop, where the user can replace his/her eyeglass frame with another one from the store and see his/her picture with the new one before buying it. Experimental results show that the proposed system effectively replaces eyeglass frames from the frontal face image of the user.

Eyeglass replacement system is presented in this work. The proposed system automatically replaces the eyeglass frame from an input frontal face image with another one. First, the system generates a mask that specifies the location of the eyeglass frame in the input image. Next, the unwanted pixels from the image are completed using patch-based synthesis. Finally, the user chooses a new eyeglass frame from a dataset of eyeglass frames to see his/her frontal image with the selected one. One of useful applications of the proposed system is an electronic eyeglasses shop, where the user can replace his/her eyeglass frame with another one from the store and see his/her picture with the new one before buying it. Experimental results show that the proposed system effectively replaces eyeglass frames from the frontal face image of the user.

Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classi?er. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data may affected by error or noise, working with this data is a challenging problem. In this paper, an approach has been proposed to overcome this problem. This method is called DSA-GEPSVM. The main improvements are carried out based on the following: 1) a novel fuzzy values in the linear case. 2) A new Kernel function in the nonlinear case. 3) Differential Search Algorithm (DSA) is reformulated to ?nd near optimal values of the GEPSVM parameters and its kernel parameters. The experimental results show that the proposed approach is able to find the suitable parameter values, and has higher classification accuracy compared with some other algorithms.

Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classi?er. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data may affected by error or noise, working with this data is a challenging problem. In this paper, an approach has been proposed to overcome this problem. This method is called DSA-GEPSVM. The main improvements are carried out based on the following: 1) a novel fuzzy values in the linear case. 2) A new Kernel function in the nonlinear case. 3) Differential Search Algorithm (DSA) is reformulated to ?nd near optimal values of the GEPSVM parameters and its kernel parameters. The experimental results show that the proposed approach is able to find the suitable parameter values, and has higher classification accuracy compared with some other algorithms.

Fast Balanced K-means (FBK-means) clustering approach is one of the most important consideration when one want to solve clustering problem of balanced data. Mostly, numerical experiments show that FBK-means is faster and more accurate than the K-means algorithm, Genetic Algorithm, and Bee algorithm. FBK-means Algorithm needs few distance calculations and fewer computational time while keeping the same clustering results. However, the FBK-means algorithm doesn’t give good results with imbalanced data. To resolve this shortage, a more efficient clustering algorithm, namely Fast K-means (FK-means), developed in this paper. This algorithm not only give the best results as in the FBK-means approach but also needs lower computational time in case of imbalance data.

Fast Balanced K-means (FBK-means) clustering approach is one of the most important consideration when one want to solve clustering problem of balanced data. Mostly, numerical experiments show that FBK-means is faster and more accurate than the K-means algorithm, Genetic Algorithm, and Bee algorithm. FBK-means Algorithm needs few distance calculations and fewer computational time while keeping the same clustering results. However, the FBK-means algorithm doesn’t give good results with imbalanced data. To resolve this shortage, a more efficient clustering algorithm, namely Fast K-means (FK-means), developed in this paper. This algorithm not only give the best results as in the FBK-means approach but also needs lower computational time in case of imbalance data.

In this paper, we present a system for video face replacement that requires only two videos of a source actor and a target actor using only a single digital camera. Existing video face replacement techniques usually need special equipment or 3D models; the proposed system achieves a realistic replacement of faces without using 3D models or special equipment. There are many applications of the proposed system that are presented in this paper using only two footages of actors. We can replace a frontal face with another one; this gives the possibility to change the appearance of actors without makeup or any prior settings. We introduce a new technique for face blending that is based on a gradient domain method called Modified Poisson Blending (MPB) to reduce the bleeding problem of Poisson image editing, and achieve realistic results of face replacement. Experimental results demonstrate that the proposed system using MPB technique produces more realistic results than the results of other cloning techniques.

In this paper, we present a system for video face replacement that requires only two videos of a source actor and a target actor using only a single digital camera. Existing video face replacement techniques usually need special equipment or 3D models; the proposed system achieves a realistic replacement of faces without using 3D models or special equipment. There are many applications of the proposed system that are presented in this paper using only two footages of actors. We can replace a frontal face with another one; this gives the possibility to change the appearance of actors without makeup or any prior settings. We introduce a new technique for face blending that is based on a gradient domain method called Modified Poisson Blending (MPB) to reduce the bleeding problem of Poisson image editing, and achieve realistic results of face replacement. Experimental results demonstrate that the proposed system using MPB technique produces more realistic results than the results of other cloning techniques.

In this paper, we present a system for video face replacement that requires only two videos of a source actor and a target actor using only a single digital camera. Existing video face replacement techniques usually need special equipment or 3D models; the proposed system achieves a realistic replacement of faces without using 3D models or special equipment. There are many applications of the proposed system that are presented in this paper using only two footages of actors. We can replace a frontal face with another one; this gives the possibility to change the appearance of actors without makeup or any prior settings. We introduce a new technique for face blending that is based on a gradient domain method called Modified Poisson Blending (MPB) to reduce the bleeding problem of Poisson image editing, and achieve realistic results of face replacement. Experimental results demonstrate that the proposed system using MPB technique produces more realistic results than the results of other cloning techniques.

Video completion has many applications in movie post-production, such as removing unwanted objects, artifacts, or logos. Most state-of-the-art video completion techniques are time-consuming in order to ensure spatial-temporal coherence. This paper presents a Fast Video Completion (FVC) technique that is based on patch-based synthesis and image registration. FVC generates key frames without the unwanted objects over the input video using a patch-based synthesis. The rest of the video completion process requires a low computing time using the registered prior frame. The results of the proposed technique show that FVC preserves the spatial-temporal coherence in an acceptable time, that makes the proposed technique is applicable for long videos.