Face Recognition using Average Half Face Template
Corresponding email: imranshahzad@ciit.net.pk
Published at : 07 Jul 2014
Volume : IJtech
Vol 5, No 2 (2014)
DOI : https://doi.org/10.14716/ijtech.v5i2.408
Shehzad, M.I., Awais, M., Amin, M., Shah, Y.A., 2014. Face Recognition using Average Half Face Template. International Journal of Technology. Volume 5(2), pp. 159-168
| Muhammad Imran Shehzad | Electrical Engineering Department, COMSATS Institute of Information Technology, University Road, Tobe Camp Postal Code 22060 Abbottabad, Pakistan |
| Muhammad Awais | Electrical Engineering Department, COMSATS Institute of Information Technology, University Road, Tobe Camp Postal Code 22060 Abbottabad, Pakistan |
| Mohsin Amin | Electrical Engineering Department, COMSATS Institute of Information Technology, University Road, Tobe Camp Postal Code 22060 Abbottabad, Pakistan |
| Yasir Ali Shah | Electrical Engineering Department, COMSATS Institute of Information Technology, University Road, Tobe Camp Postal Code 22060 Abbottabad, Pakistan |
Face recognition is one of the most important technologies, which has been well-developed over the last two decades. Face recognition technology has reached a level of utmost importance as the security issues increase worldwide. Most of the previously proposed systems, based on half face images are computationally slow and require more storage. In the proposed model, an average half face image is used for recognition to reduce computational time and storage requirements. The Viola Jones method is used in conjunction with intensity-based registration for real time face detection and registration, before splitting the full face. Finally, Principal Component Analysis (PCA) is used to compress the multi-dimensional data space and recognition. Experimental results clearly elaborate that half face recognition produces much better results as compared to the full face recognition and other previously proposed half face recognition models.
Ada Boost (Ad Boost), Face Registration, Principle Component Analysis (PCA)
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