An Analysis of Various Edge Detection Techniques on Illuminant Variant Images

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)


One of the key challenges of face recognition is varying illumination. Many feature-based methods were developed in the recent years to detect the illuminant invariant facial features. This paper mainly focuses on the effect of various edge detection algorithms on illuminant variant images. The conventional sobel and adaboost are applied. Along with that, the proposed NSCT integrated ant colony optimization (ACO) approach is also used. The proposed method comprises a normal shrink filter in NSCT domain which produces illuminant invariant for the given image. Then, to capture the important geometrical structures and to reduce the feature dimensionality, ACO algorithm is performed. This combined approach fairly detects the edges with improved quality. Finally, for recognition, a graph matching algorithm is employed. This algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. While applying the three methods to the YaleB database, experimental results show that the proposed work yields the better recognition.


Illuminant invariant Non-subsampled contourlet Feature subset Ant colony optimization Weighted graph matching 


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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Noorul Islam Centre for Higher EducationKumaracoilIndia
  2. 2.Electrical and Electronics EngineeringNoorul Islam Centre for Higher EducationKumaracoilIndia

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