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2009 International Conference on Advances in Recent Technologies in Communication and Computing
Advanced Biometric Identification on Face, Gender and Age Recognition
Kottayam, Kerala, India
October 27-October 28
ISBN: 978-0-7695-3845-7
| ASCII Text | x | ||
| Ramesha K., Srikanth N., K.B. Raja, Venugopal K.R., L.M. Patnaik, "Advanced Biometric Identification on Face, Gender and Age Recognition," Advances in Recent Technologies in Communication and Computing, International Conference on, pp. 23-27, 2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/ARTCom.2009.21, author = {Ramesha K. and Srikanth N. and K.B. Raja and Venugopal K.R. and L.M. Patnaik}, title = {Advanced Biometric Identification on Face, Gender and Age Recognition}, journal ={Advances in Recent Technologies in Communication and Computing, International Conference on}, volume = {0}, year = {2009}, isbn = {978-0-7695-3845-7}, pages = {23-27}, doi = {http://doi.ieeecomputersociety.org/10.1109/ARTCom.2009.21}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Advances in Recent Technologies in Communication and Computing, International Conference on TI - Advanced Biometric Identification on Face, Gender and Age Recognition SN - 978-0-7695-3845-7 SP23 EP27 A1 - Ramesha K., A1 - Srikanth N., A1 - K.B. Raja, A1 - Venugopal K.R., A1 - L.M. Patnaik, PY - 2009 KW - Face Recognition KW - Gender Classification KW - Age Classification KW - Wrinkle Texture KW - Artificial NeuralNetworks KW - Shape and Texture Transformation VL - 0 JA - Advances in Recent Technologies in Communication and Computing, International Conference on ER - | |||
The face recognition system attains good accuracy in personal identification when they are provided with a large set of training sets. In this paper, we proposed Advanced Biometric Identification on Face, Gender and Age Recognition (ABIFGAR)algorithm for face recognition that yields good results when only small training set is available and it works even with a raining set as small as one image per person. The process is divided into three phases: Pre-processing, Feature Extraction and Classification. The geometric features from a facial image are obtained based on the symmetry of human faces and the variation of gray levels, the positions of eyes, nose and mouth are located by applying the Canny edge operator. The gender and age are classified based on shape and texture information using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.
Index Terms:
Face Recognition, Gender Classification, Age Classification, Wrinkle Texture, Artificial NeuralNetworks, Shape and Texture Transformation
Citation:
Ramesha K., Srikanth N., K.B. Raja, Venugopal K.R., L.M. Patnaik, "Advanced Biometric Identification on Face, Gender and Age Recognition," artcom, pp.23-27, 2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009
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