Recognizing faces with PCA and ICA

By Bruce A. Draper, Kyungim Baek, M. Bartlett, J. Ross Beveridge. Computer Vision and Image Understanding, 2003

652 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c5da5ac5f201428868dc6283282f14830d7762b2

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Citations in the corpus

Shape-based hand recognition (2006, methodology): “In the context of natural images, it also serves as a useful tool for feature extraction and person authentication tasks [1], [6].”
A Survey of Face Recognition Techniques (2009, methodology): “Others have also experimented with ICA [140-146] and have reported that this technique, and variations of it, appear to perform better then PCA under most circumstances.”
Two-dimensional PCA: a new approach to appearance-based face representation and recognition (2004, methodology): “Bartlett et al. [11] and Draper et al. [12] proposed using ICA for face representation and found that it was better than PCA when cosines were used as the similarity measure (however, their performance was not significantly different if the Euclidean distance is used).”
Gabor wavelets and General Discriminant Analysis for face identification and verification (2007, methodology): “However, Baek [7] presented a comparison of PCA and ICA and concluded that, when a proper distance metric was used, PCA outperformed ICA significantly on the FERET face database [8] of more than 1000 images.”
Feature Learning and Analysis for Cleanliness Classification in Restrooms (2019, methodology): “In [15] outlined a method of combining PCA”
Personal Authentication Using Finger Knuckle Surface (2009, background): “The subspace techniques that can create spatially localized features are receiving increasing attention in the literature [20], [21].”
Independent comparative study of PCA, ICA, and LDA on the FERET data set (2005, methodology): “A sur- vey of face recognition techniques has been given by Zhao et al., (2003).”
Face recognition by independent component analysis (2002, methodology): “Indeed, this prediction has born out in recent experiments with a larger set of FERET face images [17].”
Artifact Removal in Physiological Signals—Practices and Possibilities (2012, methodology): “This method was also shown on simulated data to outperform an ICA-based technique (JADE), where the overall relative root MSE (RRMSE) for CCA was on average 2.48 times lower than that achieved using ICA.”
KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition (2005, methodology): “Is CKFD statistically significantly better than other methods in terms of its recognition rate? To answer this question, let us evaluate the experimental results in Table 3 using McNemar’s [39], [40], [41] significance test.”