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Face Recognition using Similarity Pattern of Image Directional Edge ResponseBASHAR, F. , KHAN, A. , AHMED, F. , KABIR, H.
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discrete cosine transform, face recognition, feature extraction, image texture analysis, pattern analysis
recognition(31), face(25), pattern(17), local(11), analysis(10), image(8), binary(6), vision(5), machine(5), information(5)
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About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 69 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01011
Web of Science Accession Number: 000332062300011
SCOPUS ID: 84894635007
An effective face descriptor is critical for a successful face recognition system and must overcome the challenges of changing environment. The face representation must have discriminatory information and be computationally feasible for any face recognition system. In this paper we propose a new face descriptor, Similarity Pattern of Image Directional Edge Response (SPIDER), for face recognition. An image is divided into smaller local regions and 8 directional edge responses are generated for each pixel position in the regions. The regional cumulative response of each direction is calculated and a histogram is generated consisting of 8 bins, one for each of the directions. The SPIDER code is generated by calculating the similarity between the histogram of the local region around each pixel against the histogram of neighbor regions. The feature vector is projected to a low-dimension vector space using a dimension reduction method to minimize the classification time. Experiments using the proposed method were carried out on the FERET database and results show improved recognition rates indicating the robustness to changing environment, and a low classification time compared to the existing methods.
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 A Low Cost Structurally Optimized Design for Diverse Filter Types, Kazmi, Majida, Aziz, Arshad, Akhtar, Pervez, Ikram, Nassar, Zeng, Li, PLOS ONE, ISSN 1932-6203, Issue 11, Volume 11, 2016.
Digital Object Identifier: 10.1371/journal.pone.0166056 [CrossRef]
 Gradient-Orientation-Based PCA Subspace for Novel Face Recognition, Ghinea, Gheorghita, Kannan, Rajkumar, Kannaiyan, Suresh, IEEE Access, ISSN 2169-3536, Issue , 2014.
Digital Object Identifier: 10.1109/ACCESS.2014.2348018 [CrossRef]
 Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic Histograms, SRIKOTE, G., MEESOMBOON, A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 16, 2016.
Digital Object Identifier: 10.4316/AECE.2016.03015 [CrossRef] [Full text]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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