Titlebook: Handbook of Face Recognition; Stan Z. Li,Anil K. Jain,Jiankang Deng Book 2024Latest edition The Editor(s) (if applicable) and The Author(s

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https://doi.org/10.1007/978-3-663-05163-3tworks (DNNs) [18, 37, 39]. Empowered by the excellent performance of DNNs, face recognition models are widely deployed in various safety-critical scenarios ranging from finance/payment to automated surveillance systems. Despite its booming development, recent research in adversarial machine learnin

吞噬

荒唐

corpus-callosum

Face Recognition Research and Developmenth topics in computer vision with great commercial applications?[., ., ., .], like biometric authentication, financial security, access control, intelligent surveillance, etc. Because of its commercial potential and practical value, face recognition has attracted great interest from both academia and

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Convolutional Neural Networks and Architecturescomes the most popular deep neural network model to process visual data, including images and videos. Until now, a lot of modern fundamental computer vision systems, e.g., image classification and face recognition, are usually built upon convolutional networks.

漂白

Generative Networksmodels to design generative models for facial images. However, the abstraction and distortion of predefined models hinder the realism of the generated faces. With the development of deep learning, a large number of generative models have been proposed, especially in the field of face image generatio

秘密會議

ANA

Facial Attribute Analysisted facial attribute analysis has become an active field in the area of biometric recognition due to its wide range of possible applications, such as face verification?[., .], face identification?[., .], or surveillance?[.], just to mention a few.

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Face Feature Embeddingty. To implement face verification and recognition at scale, it is indispensable to have a discriminative face embedding in which faces of the same person have small distances and faces of different people have large distances. Once such embedding is obtained, face verification involves thresholding

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Video-Based Face Recognitioncontent analysis, etc. Compared to still face recognition, video-based face recognition is more challenging due to a much larger amount of data to be processed and significant intra/inter-class variations caused by motion blur, low video quality, occlusion, frequent scene changes, and unconstrained