Face Recognition Using 2D and 3D Facial Data

By K. I. Chang, Kevin W. Bowyer, Patrick J. Flynn. 2003

362 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/68e2da4588c49e7d6f8eb62867208b14adbc3882

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Citations in the corpus

An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition (2007, methodology): “Chang et al. [9] used a PCA-based approach for separate 2D and 3D face recognition and fused the matching scores.”
FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers (2019, background): “Face recognition involves comparing features extracted from a pair of face images, using a face matcher, to determine their degree of similarity or dissimilarity [1], [2].”
2D and 3D face recognition: A survey (2007, background): “Recently Chang et al. (2003) investigated on possible improvements that 2D face biometric can receive integrating the 3D also.”
Fusing Multiple Features for Depth-Based Action Recognition (2015, background): “Data fusion has been studied extensively, and shown great performance in many areas, 2010], human identiﬁcation [Ben-Yacoub et al. 1999], face recognition [Chang et al. 2003], handwriting recognition [Xu et al. 1992], biometrics [Ross and Jain 2003], and so forth.”
Handbook of Face Recognition, 2nd Edition (2011, methodology): “Our experiments are conducted on a subset of the ND1 face database [10], which contains 534 images from 200 subjects.”
Multimodal biometrics: An overview (2004, background): “[7] acquire both 2D and 3D images of the face and combine them at the data level as well as the match score level to improve the performance of a face recognition system.”
An evaluation of multimodal 2D+3D face biometrics (2005, background): “The rank-one recognition rates are generally higher in the case of training with the larger number of images, and these are the values listed outside the parentheses in Table 1.”
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition (2006, methodology): “They use portions of the dataset used in [ 14 ] in their experiments.”
A Survey of Face Recognition Techniques (2009, methodology): “Such methods make use of the PCA of intensity images [242-244], facial profile intensities [245], Iterative Closest Point (ICP [246]) [247, 248], Gabor wavelets [249], and Local Feature Analysis [250], etc.”
Three-Dimensional Face Recognition (2005, methodology): “Achermann et al. (1997), Hesher et al. (2003), Mavridis et al. (2001), Chang et al. (2003), and Tsalakanidou et al. (2003) explored the extension of conventional dimensionality reduction techniques, like Principal Component Analysis (PCA), to range images or combination of intensity and range…”