References

Jeeves, M. A.

doi:10.1007/978-94-009-4420-6_46

Cited by 1 publication

(2 citation statements)

References 487 publications

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“…The location information is not used for vector similarity calculation, but only the magnitudes of the wavelet coefficients takes place at equation (1). Although phase compensated similarity function is found to increase recognition performance significantly similarity function without phase is chosen to avoid computational complexity.…”

Section: Matching Proceduresmentioning

confidence: 99%

“…Similarity of the two feature vectors is greater than th 2 , where th 2 is chosen as the standard deviation of similarities of all feature vectors in the reference gallery and the similarity of two vectors is computed by equation (1). In other words, increasing th 1 gives more area for searching the feature points with similarities larger than th 2 .…”

Section: Matching Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Facial Prediction and Recognition Using Wavelets Transform Algorithm and Technique

Okuwobi

2014

AMM

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An efficient facial representation is a crucial step for successful and effective performance of cognitive tasks such as object recognition, fixation, facial recognition system, etc. This paper demonstrates the use of Gabor wavelets transform for efficient facial representation and recognition. Facial recognition is influenced by several factors such as shape, reflectance, pose, occlusion and illumination which make it even more difficult. Gabor wavelet transform is used for facial features vector construction due to its powerful representation of the behavior of receptive fields in human visual system (HVS). The method is based on selecting peaks (high-energized points) of the Gabor wavelet responses as feature points. This paper work introduces the use of Gabor wavelets transform for efficient facial representation and recognition. Compare to predefined graph nodes of elastic graph matching, the approach used in this paper has better representative capability for Gabor wavelets transform. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. Based on the experiment, the proposed method performs better compared to the graph matching and eigenface based methods. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. The proposed system is validated using four different face databases of ORL, FERRET, Purdue and Stirling database.

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Section: Matching Proceduresmentioning

confidence: 99%

Section: Matching Proceduresmentioning

confidence: 99%