Face authentication test on the BANCA database

Messer, Kieron; Kittler, Josef; Sadeghi, Mohammad Taghi; Hamouz, M.; Kostin, A.; Cardinaux, Fabien; Marcel, Sébastien; Bengio, Samy; Sanderson, Conrad; Poh, Norman; Rodriguez, Yann; Czyz, J.; Vandendorpe, Luc; McCool, Chris; Lowther, Scott; Sridharan, Sridha; Chandran, Vinod; Palacios, Roberto Paredes; Vidal, Enrique; Li, Bai; Shen, Lianfeng; Wang, Yan; Chiang, Yueh-Hsuan; Liu, Hsien-Chang; Hung, Yi‐Ping; Heinrichs, A.; Müller, Martín; Tewes, Andreas; Malsburg, Christoph von der; Würtz, Rolf P.; Wang, Zhenger; Feng, Xue; Ma, Yan; Yang, Qiong; Fang, Chi; Ding, Xiaoqing; Lucey, Simon; Goss, Ryan G.; Schneiderman, Henry

doi:10.1109/icpr.2004.1333826

Cited by 72 publications

(47 citation statements)

References 25 publications

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“…Since the same database and protocol was used in our experiments, our verification results are directly comparable with those of the participants in the competition, providing a useful focus for further discussion. Table 4 shows the EER of a number of popular techniques on both the development set and evaluation set, which are directly extracted from the test report [20]. The results are very encouraging, with the EER returned by our method on both the development set and evaluation set seen to be significantly better than many popular methods reported in the literature, such as LDA, Elastic Bunch Graph Matching, HMM, etc.…”

Section: Comparison With Other Methodsmentioning

confidence: 48%

“…Gabor features have also shown robustness against variations of head pose and camera orientation The generalization ability of the novel Gabor + GDA method has also been observed. Since the FERET [8] and BANCA database [19,20], specially designed to test face identification and verification algorithms, are used for the evaluation of our algorithms, our results are directly comparable with other methods, and comparisons with a number of popular approaches will be made to illustrate the advantages of the proposed new method.…”

Section: Introductionmentioning

confidence: 71%

“…The 2004 international face verification competition [20], held in conjunction with the International Conference on Pattern Recognition (ICPR) 2004, has been adopted as an informal benchmark for comparisons within the research community. The competition adopted the BAN- CA database and protocol P for evaluation.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Gabor wavelets and General Discriminant Analysis for face identification and verification

Shen

Fairhurst

2007

Image and Vision Computing

241

139

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A novel and uniform framework for both face identification and verification is presented in this paper. The framework is based on a combination of Gabor wavelets and General Discriminant Analysis, and can be considered appearance based in that features are extracted from the whole face image. The feature vectors are then subjected to subspace projection. The design of Gabor filters for facial feature extraction is also discussed, which is seldom reported in the literature. The method has been tested extensively for both identification and verification applications. The FERET and BANCA face databases were used to generate the results. Experiments show that Gabor wavelets can significantly improve system performance whilst General Discriminant Analysis outperforms other subspace projection methods such as Principal Component Analysis, Linear Discriminant Analysis, and Kernel Principal Component Analysis. Our method has achieved 97.5% recognition rate on the FERET database, and 5.96% verification error rate on the BANCA database. This is a significantly better performance than that attainable with other popular approaches reported in the literature. In particular, our verification system performed better than most of the systems in the 2004 International Face Verification Competition, using the BANCA face database and specially designed test protocols.

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Section: Comparison With Other Methodsmentioning

confidence: 48%

Section: Introductionmentioning

confidence: 71%

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Gabor wavelets and General Discriminant Analysis for face identification and verification

Shen

Fairhurst

2007

Image and Vision Computing

241

139

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“…Face recognition has a well-established history in computer vision. Differently to many other object recognition areas, stiff competitive tests are carried out on widely available image galleries (e.g., Phillips et al 2000, Messer et al 2004, Phillips et al 2005. The existence of such tests and databases allows objective judgment of the performance of single systems.…”

Section: Face Recognitionmentioning

confidence: 99%

Information Routing, Correspondence Finding, and Object Recognition in the Brain

Wolfrum¹

2010

Studies in Computational Intelligence

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This work would not have been possible without the people I have had the honor of working with over the past years. First of all I want to thank Christoph von der Malsburg, whose scientific concepts have inspired large parts of this thesis, and who granted me enough freedom to follow my own research directions while at the same time providing motivation whenever this was necessary. I also thank Rudolf Mester for good discussions that helped me retain my engineering viewpoint on the interdisciplinary problems that I worked on. Discussions with Geoff Goodhill, Bruno Olshausen, Jochen Triesch, and Alan Yuille, among others, have shaped my thinking and had an important impact on this thesis.I thank Urs Bergmann, Jenia Jitsev, and Junmei Zhu for proof-reading parts of the thesis, and Jörg Lücke for good collaboration. The neurogroups at FIAS have provided a rich and stimulating environment, it has been fun working and thinking with you! I also thank all colleagues at FIAS for good company and the truly interdisciplinary interaction we had (e.g. at Hirschegg). Last but not least I owe this thesis to my parents, who planted curiosity and a desire for understanding in me that were stronger than the difficulties I met during this dissertation. iv

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“…Shen and Bai (2004b) extended the Gabor feature space to the kernel space to achieve better classification performance than linear dimension reduction methods. Their method was one of the top two performers in the 2004 face verification competition (Messer et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

MutualBoost learning for selecting Gabor features for face recognition

Shen

2006

Pattern Recognition Letters

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This paper describes an improved boosting algorithm, the MutualBoost algorithm, and its application in developing a fast and robust Gabor feature based face recognition system. The algorithm uses mutual information to eliminate redundancy among Gabor features selected using the AdaBoost algorithm. Selected Gabor features are then subjected to Generalized Discriminant Analysis (GDA) for class separability enhancement before being used for face recognition. Compared with one of the top performers in the 2004 face verification competition, our method demonstrates clear advantages in classification accuracy, memory and computation. The method has been tested on the whole FERET database using the FERET evaluation protocol. Significant improvement in performance is observed. For example, existing Gabor based methods use a huge number of Gabor features, our method needs only hundreds of Gabor features to achieve very high classification accuracy. Due to substantially reduced feature dimension, memory and computation costs are reduced significantly -only 4 s are needed to recognize 200 face images.

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Face authentication test on the BANCA database

Cited by 72 publications

References 25 publications

Gabor wavelets and General Discriminant Analysis for face identification and verification

Gabor wavelets and General Discriminant Analysis for face identification and verification

Information Routing, Correspondence Finding, and Object Recognition in the Brain

MutualBoost learning for selecting Gabor features for face recognition

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