TY - JOUR
T1 - Face recognition using adjacent pixel intensity difference quantization histogram combined with markov stationary features
AU - Lee, Feifei
AU - Kotani, Koji
AU - Chen, Qiu
AU - Ohmi, Tadahiro
PY - 2012/6/1
Y1 - 2012/6/1
N2 - Previously, we have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. After the intensity variation vectors for all the pixels in an image are calculated, each vector is quantized directly in (dIx, dIy) plane instead of r-θ plane. By counting the number of elements in each quantized area in the (dIx, dIy) plane, a histogram can be created. This histogram, obtained by APIDQ for facial images, is utilized as a very effective personal feature. In this paper, we combine the APIDQ histogram with Markov Stationary Features (MSF) so as to add spatial structure information to histogram. Experimental results show that the combined MSF-APIDQ feature is more robust than that just using original APIDQ histogram. Furthermore, Top 1 recognition rate of 98.2% is achieved by using FB task of the publicly available face database of FERET.
AB - Previously, we have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. After the intensity variation vectors for all the pixels in an image are calculated, each vector is quantized directly in (dIx, dIy) plane instead of r-θ plane. By counting the number of elements in each quantized area in the (dIx, dIy) plane, a histogram can be created. This histogram, obtained by APIDQ for facial images, is utilized as a very effective personal feature. In this paper, we combine the APIDQ histogram with Markov Stationary Features (MSF) so as to add spatial structure information to histogram. Experimental results show that the combined MSF-APIDQ feature is more robust than that just using original APIDQ histogram. Furthermore, Top 1 recognition rate of 98.2% is achieved by using FB task of the publicly available face database of FERET.
KW - Adjacent pixel intensity difference quantization (APIDQ)
KW - Face recognition
KW - Histogram feature
KW - Markov stationary features (MSF)
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UR - http://www.scopus.com/inward/citedby.url?scp=84865341186&partnerID=8YFLogxK
U2 - 10.4156/ijact.vol4.issue 12.38
DO - 10.4156/ijact.vol4.issue 12.38
M3 - Article
AN - SCOPUS:84865341186
VL - 4
SP - 327
EP - 335
JO - International Journal of Advancements in Computing Technology
JF - International Journal of Advancements in Computing Technology
SN - 2005-8039
IS - 12
ER -