NDLI: Improving Face Recognition Performance Using Similarity Feature-Based Selection and Classification Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Chi-Kien Tran Tsair-Fwu Lee Chiu-Ching Tuan Chi-Heng Lu Pei-Ju Chao
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan (Chi-Kien Tran; Tsair-Fwu Lee) \|\| Dept. of Electron. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan (Chiu-Ching Tuan; Chi-Heng Lu) \|\| Dept. of Radiat. Oncology, Kaohsiung Chang Gung Memorial Hosp., Kaohsiung, Taiwan (Pei-Ju Chao)
Abstract	In this paper, we propose the effective similarity feature-based selection and classification algorithm to select similarity features on the training images and to classify face images in face recognition system. The experiments are conducted on The ORL Database of Faces, which consists of 400 images of 40 individuals. Two face recognition systems, one based on the histogram-based feature, and the other based on the feature which is the mean of pixel values in window with size of 4×4 (M4×4), are developed. Euclidean distance and Manhattan distance are taken as distance metrics for the classification method. The results indicated that the proposed algorithms not only reduce the dimensions of feature space, but also achieve a mean recognition accuracy that is 1.55% ± 11.31% better compared to conventional algorithms.
Starting Page	56
Ending Page	60
File Size	176762
Page Count	5
File Format	PDF
e-ISBN	9781479931842
DOI	10.1109/RVSP.2013.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Similarity feature Histograms Histogram Face recognition Pixel values Euclidean distance Feature extraction Vectors Classification algorithms Face Manhattan distance
Content Type	Text
Resource Type	Article

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