Alaa Eleyan

Work place: Electrical & Electronic Engineering Department, Mevlana University, Konya, Turkey



Research Interests: Image Processing, Image Manipulation, Image Compression, Computer Graphics and Visualization, Computer Vision, Computer systems and computational processes


ELEYAN Alaa (1979-) is an assistant professor in the Electrical & Electronic Engineering Department at Mevlana University in Konya, Turkey. He is the founder and the head of the Video, Image and Speech Processing VISP research group. His research interests include face recognition, pattern classification and texture retrieval. Dr. Eleyan obtained his B.Sc. & M.Sc. from Near East University and Ph.D. from Eastern Mediterranean University in Northern Cyprus in 2002, 2004 & 2009, respectively. 

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

By Musa M.Ameen Alaa Eleyan

DOI:, Pub. Date: 8 Oct. 2017

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.

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Enhanced Face Recognition using Data Fusion

By Alaa Eleyan

DOI:, Pub. Date: 8 Dec. 2012

In this paper we scrutinize the influence of fusion on the face recognition performance. In pattern recognition task, benefiting from different uncorrelated observations and performing fusion at feature and/or decision levels improves the overall performance. In features fusion approach, we fuse (concatenate) the feature vectors obtained using different feature extractors for the same image. Classification is then performed using different similarity measures. In decisions fusion approach, the fusion is performed at decisions level, where decisions from different algorithms are fused using majority voting. The proposed method was tested using face images having different facial expressions and conditions obtained from ORL and FRAV2D databases. Simulations results show that the performance of both feature and decision fusion approaches outperforms the single performances of the fused algorithms significantly.

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