2014 12th International Conference on Frontiers of Information Technology (FIT) (2014)
Dec. 17, 2014 to Dec. 19, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2014.56
In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms studied in this work are Principal Component Analysis (PCA) and AdaBoost with Linear Discriminant Analysis (LDA) as a weak learner. Images from multi-pie database are used for evaluation. Simulation results revealed that given one gallery (Training) face image and four different pose images as a probe (Testing), PCA based system is more accurate in recognizing pose, while AdaBoost was more robust on recognizing low resolution images.
Principal component analysis, Face, Face recognition, Accuracy, Databases, Image recognition, Training,PCA, AdaBoost, LDA
Zahid Mahmood, Tauseef Ali, Shahid Khattak, Samee U. Khan, "A Comparative Study of Baseline Algorithms of Face Recognition", 2014 12th International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 263-268, 2014, doi:10.1109/FIT.2014.56