Face Recognition using Segmental Euclidean Distance

Farrukh Sayeed; Madasu Hanmandlu; Abdul Quaiyum Ansari

doi:10.14429/dsj.61.1178

Farrukh Sayeed PA College of Engineering, Mangalore
Madasu Hanmandlu Indian Institute of Technology Delhi, New Delhi
Abdul Quaiyum Ansari Jamia Millia Islamia, New Delhi

DOI: https://doi.org/10.14429/dsj.61.1178

Keywords: Face Segmentation, principal component analysis, DCT and fuzzy features, segmental euclidean

Abstract

In this paper an attempt has been made to detect the face using the combination of integral image along with the cascade structured classifier which is built using Adaboost learning algorithm. The detected faces are then passed through a filtering process for discarding the non face regions. They are individually split up into five segments consisting of forehead, eyes, nose, mouth and chin. Each segment is considered as a separate image and Eigenface also called principal component analysis (PCA) features of each segment is computed. The faces having a slight pose are also aligned for proper segmentation. The test image is also segmented similarly and its PCA features are found. The segmental Euclidean distance classifier is used for matching the test image with the stored one. The success rate comes out to be 88 per cent on the CG(full) database created from the databases of California Institute and Georgia Institute. However the performance of this approach on ORL(full) database with the same features is only 70 per cent. For the sake of comparison, DCT(full) and fuzzy features are tried on CG and ORL databases but using a well known classifier, support vector machine (SVM). Results of recognition rate with DCT features on SVM classifier are increased by 3 per cent over those due to PCA features and Euclidean distance classifier on the CG database. The results of recognition are improved to 96 per cent with fuzzy features on ORL database with SVM.

Defence Science Journal, 2011, 61(5), pp.431-442, DOI:http://dx.doi.org/10.14429/dsj.61.1178

Author Biographies

Farrukh Sayeed, PA College of Engineering, Mangalore

Mr Farrukh Sayeed has obtained his BTech and MTech from Aligarh Muslim University (AMU), Aligarh and National Institute of Technology (NIT)Jamshedpur, respectively. Presently pursuing PhD from Department of Electrical Engineering, Jamia Millia Islamia, New Delhi. He is a faculty in the Department of Electronics & Communication Engineering in P.A. College of Engineering, Mangalore.

Madasu Hanmandlu, Indian Institute of Technology Delhi, New Delhi

Dr Madasu Hanmandlu received his MTech (Power systems) from REC Warangal, Jawaharlal Nehru Technological University (JNTU), India, in 1976, and PhD (Control Systems) from Indian Institute of Technology(IIT), Delhi, India, in 1981. Presently working as Professor in Department of Electrical Engineering, IIT, Delhi. He worked in the areas of Power Systems, Control, Robotics and Computer Vision, before shifting to fuzzy theory. His current research interests mainly include: Fuzzy modeling for dynamic systems and applications of fuzzy logic to image processing, document processing, medical imaging, multimodal biometrics, surveillance and intelligent control.He has authored a book on Computer Graphics and published more than 220 publications in both conferences and journals.He has guided 15 PhDs and 100 MTech students.

Abdul Quaiyum Ansari, Jamia Millia Islamia, New Delhi

Dr Abdul Quaiyum Ansari received his BTech, MTech and PhD from AMU, Aligarh; IIT Delhi, New Delhi, and JMI, New Delhi, respectively. He is a Professor and Head, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi. His research areas includes: Computer networks, image processing and fuzzy logic. He is on the advisory board and editorial board of many international and national Journals.

Face Recognition using Segmental Euclidean Distance

Abstract

Author Biographies

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