Score Fusion of SIFT & SURF Descriptors for Face Recognition Using Wavelet Transforms

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Musa M.Ameen 1,* Alaa Eleyan 2

1. Ishik University/Computer Engineering Department, Erbil, 44001, Iraq

2. Avrasya University/Electrical and Electronics Engineering Department, Trabzon, 61000, Turkey

* Corresponding author.


Received: 9 Jun. 2017 / Revised: 3 Aug. 2017 / Accepted: 12 Sep. 2017 / Published: 8 Oct. 2017

Index Terms

Speeded-Up Robust Features, Scale-Invariant Feature Transform, Discrete Wavelet Transform, Gabor Wavelet Transform


Automatic face recognition is a major research area in computer vision which aims to recognize human face without human intervention. Significant developments in this field have shown that in many face recognition applications the automated techniques outperform humans. The conventional Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) are used in face recognition where they provide high performances. However, this performance can be improved further by transforming the input into different domains before applying SIFT and SURF algorithms. Hence, we apply Discrete Wavelet Transform (DWT) or Gabor Wavelet Transform (GWT) at the input face images, which provides denser and extra information to be used by the conventional SIFT or SURF algorithms. Matching scores of SIFT or SURF from each subimage is fused before making final decision.  Simulations show that the proposed approaches based on wavelet transforms using SIFT or SURF provides very high performance compared to the conventional algorithms.

Cite This Paper

Musa M.Ameen, Alaa Eleyan," Score Fusion of SIFT & SURF Descriptors for Face Recognition Using Wavelet Transforms", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.10, pp. 22-28, 2017. DOI: 10.5815/ijigsp.2017.10.03


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