Discriminative Multi-Projection Vectors: Modifying the Discriminative Common Vectors approach for face verification

DelPozo-Baños, Marcos; Travieso, Carlos M.; Alonso, Jesús B.; Ferrer, Miguel A.

doi:10.1109/ccst.2010.5678716

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Yale Face Database1

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Cited by 2 publications

(1 citation statement)

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“…The remaining candidate images were not changed and the background was left intact. 18.0% 1 Pozo-Banos [19] 4.34% 2 Ruiz-del-Solar [20] 7.7% 2 Aly [21] 9.9% 2 Liu [22] 29.0% 3 Cheng [23] 16.84% 3 Du [24] 33.55% 3 Li [25] 24.5% 4 Quintiliano [26] 17.0% 4 Rziza [27] 6.67% 5 Gudivada [28] 5.16% 5 Lu [29] 5.0% 5 Aroussi [30] 2.22% 5 Qi [31] 7.78% 5 Xia [32] 11.3% 6 Hua [33] 34.2% 8 Tang [34] 13.9% . The grey level gradient is quantized into the four directions 0°, 90°, 180° and 270°.…”

Section: Yale Face Databasementioning

confidence: 99%

Face recognition by detection of matching cliques of points

Stentiford

2014

SPIE Proceedings

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This paper addresses the problem of face recognition using a graphical representation to identify structure that is common to pairs of images. Matching graphs are constructed where nodes correspond to image locations and edges are dependent on the relative orientation of the nodes. Similarity is determined from the size of maximal matching cliques in pattern pairs. The method uses a single reference face image to obtain recognition without a training stage. The Yale Face Database A is used to compare performance with earlier work on faces containing variations in expression, illumination, occlusion and pose and for the first time obtains a 100% correct recognition result.

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