Handbook of Face Recognition, 2nd Edition

By Stan Z. Li, Anil K. Jain. 2011

1,038 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset (2015, background): “A thorough survey of this topic is beyond the scope of this paper, and we refer the readers to the survey papers/books [9], [10] for a comprehensive review of this problem.”
Past, Present, and Future of Face Recognition: A Review (2020, background): “Automated facial recognition technologies have increased the efficiency of judicial employees and streamlined the comparison process [7].”
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models (2014, background): “Many algorithms for face registration have been reported in the literature, for example (Li and Jain 2011), which are generic in nature and perform best with frontal images.”
Demographic Estimation from Face Images: Human vs. Machine Performance (2015, background): “The identification-specific characteristics of face images have been well explored in face recognition research and various applications [1], e.”
Face Anti-spoofing: Visual Approach (2014, background): “It is a well-known fact that poor illumination conditions, pose, and aging among other variations can deteriorate substantially the capacity to recognize individuals [11].”
How Computers See Gender (2019, background): “Perhaps the most salient example is automated facial analysis technology (FA) [19], an umbrella term for computer vision methods that use machine learning (ML) techniques to automate problems related to reading the human face [63].”
Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks (2020, background): “as proved to be a beneﬁcial modality for biometric authentication. One of the main reasons for the widespread use of face recognition systems is its nonintrusive nature of acquisition and ease of use [1]. Face recognition systems have matured a lot in recent years, and several approaches have report”
AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces (2017, background): “e task for humans, however, it is still an active research problem that draws the attention of many researchers in various elds, including computer vision and machine learning, with many applications [1, 2], such as visual surveillance, intelligent human-computer interaction, social media, demograph”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “must naturally be tailored to the unique features of the wireless environment, which is evidently quite different from the traditional applications of AI, such as in robotics and computer vision [27]–[30]. For instance, artiﬁcial intelligence is expected to play several roles in the next-generation ”