Handbook of face recognition

By Yajie Tian, Takeo Kanade, Jeffrey F. Cohn. 2003

1,228 citations recorded by the source corpus.

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Citations in the corpus

How Image Degradations Affect Deep CNN-Based Face Recognition? (2016, methodology): “In this paper, we present a systematic evaluation to assess the effect of image quality, which is listed as one of the main factors that limits the performance of the face recognition algorithms [JL11], on deep CNN-based face recognition approaches’ performance.”
Face spoofing detection from single images using micro-texture analysis (2011, background): “Despite the great deal of progress during the recent years [4], 2D face biometrics (that is identifying individuals based on their 2D face information) is still a major area of research.”
Biometrics: a tool for information security (2006, background): “The most popular approaches to face recognition [7] are based on either: 1) the location and shape of facial attributes, such as the eyes, eyebrows, nose, lips, and chin and their spatial relationships or 2) the overall (global) analysis of the face image that represents a face as a weighted…”
Recognition of blurred faces using Local Phase Quantization (2008, background): “These problems are interesting from the viewpoints of basic research aiming to efficient descriptors for facial images and of applications such as surveillance and human-computer interaction [2].”
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.g., access control, video surveillance , and criminal investigation.”
AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces (2017, background): “Gender classification is undoubtedly a simple task for humans, however, it is still an active research problem that draws the attention of many researchers in various fields, including computer vision and machine learning, with many applications [1, 2], such as visual surveillance, intelligent human”
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order (2017, methodology): “Appearance-based methods work with feature vectors extracted from the whole face, or from specific regions; these feature vectors are acquired using image filters applied to the whole face image [3].”
LBP − T OP based countermeasure against face spooﬁng attacks (2012, background): “Advances in the area were extensively reported in [8] and [16].”
Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection (2014, methodology): “The release of the public-domain database Labeled Faces in the Wild5 (LFW) in 2007 spurred interest and progress in unconstrained face recognition.”
Real-time classification of evoked emotions using facial feature tracking and physiological responses (2008, background): “For a more detailed review of the state of the art of current systems, see Li and Jain (2005) or Lyons and Bartneck (2006).”
Age , Gender and Race Estimation from Unconstrained Face Images (2014, background): “Over the past 50 years, significant advances have been made on extracting discriminative features in a face image to determine the subject’s identity [1].”
Facial Expression Recognition (2009, methodology): “Numerous techniques have been developed for face detection in still images [8, 9], (see ▶ Face Localization).”
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.”