Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective ...An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.展开更多
Face reshaping aims to adjust the shape of a face in a portrait image to make the face aesthetically beautiful,which has many potential applications.Existing methods 1)operate on the pre-defined facial landmarks,leadi...Face reshaping aims to adjust the shape of a face in a portrait image to make the face aesthetically beautiful,which has many potential applications.Existing methods 1)operate on the pre-defined facial landmarks,leading to artifacts and distortions due to the limited number of landmarks,2)synthesize new faces based on segmentation masks or sketches,causing generated faces to look dissatisfied due to the losses of skin details and difficulties in dealing with hair and background blurring,and 3)project the positions of the deformed feature points from the 3D face model to the 2D image,making the results unrealistic because of the misalignment between feature points.In this paper,we propose a novel method named face shape transfer(FST)via semantic warping,which can transfer both the overall face and individual components(e.g.,eyes,nose,and mouth)of a reference image to the source image.To achieve controllability at the component level,we introduce five encoding networks,which are designed to learn feature embedding specific to different face components.To effectively exploit the features obtained from semantic parsing maps at different scales,we employ a straightforward method of directly connecting all layers within the global dense network.This direct connection facilitates maximum information flow between layers,efficiently utilizing diverse scale semantic parsing information.To avoid deformation artifacts,we introduce a spatial transformer network,allowing the network to handle different types of semantic warping effectively.To facilitate extensive evaluation,we construct a large-scale high-resolution face dataset,which contains 14,000 images with a resolution of 1024×1024.Superior performance of our method is demonstrated by qualitative and quantitative experiments on the benchmark dataset.展开更多
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
文摘An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.
文摘Face reshaping aims to adjust the shape of a face in a portrait image to make the face aesthetically beautiful,which has many potential applications.Existing methods 1)operate on the pre-defined facial landmarks,leading to artifacts and distortions due to the limited number of landmarks,2)synthesize new faces based on segmentation masks or sketches,causing generated faces to look dissatisfied due to the losses of skin details and difficulties in dealing with hair and background blurring,and 3)project the positions of the deformed feature points from the 3D face model to the 2D image,making the results unrealistic because of the misalignment between feature points.In this paper,we propose a novel method named face shape transfer(FST)via semantic warping,which can transfer both the overall face and individual components(e.g.,eyes,nose,and mouth)of a reference image to the source image.To achieve controllability at the component level,we introduce five encoding networks,which are designed to learn feature embedding specific to different face components.To effectively exploit the features obtained from semantic parsing maps at different scales,we employ a straightforward method of directly connecting all layers within the global dense network.This direct connection facilitates maximum information flow between layers,efficiently utilizing diverse scale semantic parsing information.To avoid deformation artifacts,we introduce a spatial transformer network,allowing the network to handle different types of semantic warping effectively.To facilitate extensive evaluation,we construct a large-scale high-resolution face dataset,which contains 14,000 images with a resolution of 1024×1024.Superior performance of our method is demonstrated by qualitative and quantitative experiments on the benchmark dataset.
