TY - JOUR
T1 - Face recognition algorithm for photographs and viewed sketch matching using score-level fusion
AU - Cho, So Ra
AU - Nam, Gi Pyo
AU - Park, Kang Ryoung
PY - 2012/9/24
Y1 - 2012/9/24
N2 - For criminal searches, the necessity of matching photographs with sketches is increasing. Previously, matching was performed manually by a human observer, a time-consuming process whose accuracy can be affected by the level of human expertise. Therefore, we propose a new face recognition algorithm for photographs and sketches. This research is novel in the following three ways. First, to overcome the decrease in matching accuracy due to pose and illumination variation, we use eye alignment and retinex filtering to normalize pose, size and illumination. Second, we compare the performance of various face recognition methods, such as principal component analysis (PCA), local binary pattern (LBP), local non-negative matrix factorization (LNMF), support vector machine-discriminant analysis (SVM-DA) and modified census transform (MCT), for the matching of photographs and viewed sketches. Third, these five face recognition methods are combined on the basis of scorelevel fusion to enhance matching accuracy, thereby overcoming the performance limitations of single face recognition methods. Experimental results using a CUHK dataset showed that the accuracy of the proposed method is better than that of uni-modal face recognition methods.
AB - For criminal searches, the necessity of matching photographs with sketches is increasing. Previously, matching was performed manually by a human observer, a time-consuming process whose accuracy can be affected by the level of human expertise. Therefore, we propose a new face recognition algorithm for photographs and sketches. This research is novel in the following three ways. First, to overcome the decrease in matching accuracy due to pose and illumination variation, we use eye alignment and retinex filtering to normalize pose, size and illumination. Second, we compare the performance of various face recognition methods, such as principal component analysis (PCA), local binary pattern (LBP), local non-negative matrix factorization (LNMF), support vector machine-discriminant analysis (SVM-DA) and modified census transform (MCT), for the matching of photographs and viewed sketches. Third, these five face recognition methods are combined on the basis of scorelevel fusion to enhance matching accuracy, thereby overcoming the performance limitations of single face recognition methods. Experimental results using a CUHK dataset showed that the accuracy of the proposed method is better than that of uni-modal face recognition methods.
KW - Face recognition
KW - Photographs
KW - Viewed sketches
UR - http://www.scopus.com/inward/record.url?scp=84868092772&partnerID=8YFLogxK
U2 - 10.5772/51470
DO - 10.5772/51470
M3 - Article
AN - SCOPUS:84868092772
SN - 1729-8806
VL - 9
JO - International Journal of Advanced Robotic Systems
JF - International Journal of Advanced Robotic Systems
M1 - 84
ER -