TY - GEN
T1 - Face detection using discriminating feature analysis and support vector machine in video
AU - Shih, Peichung
AU - Liu, Chengjun
PY - 2004
Y1 - 2004
N2 - This paper presents a novel face detection method in video by using Discriminating Feature Analysis (DFA) and Support Vector Machine (SVM). Our method first incorporates temporal and skin color information to locate the field of interests. Then the face class is modelled using a small training set and the nonface class is defined by choosing nonface images that lie close to the face class. Finally, the SVM classifier together with Bayesian statistical analysis procedure applies the efficient features defined by DFA for face and nonface classification. Experiments using both still images and video streams show the feasibility of our new face detection method. In particular, when using 92 images (containing 282 faces) from the MIT-CMU test sets, our method achieves 98.2% correct face detection accuracy with 2 false detections. When using video streams, our method detects faces reliably with computational efficiency of more than 20 frames per second.
AB - This paper presents a novel face detection method in video by using Discriminating Feature Analysis (DFA) and Support Vector Machine (SVM). Our method first incorporates temporal and skin color information to locate the field of interests. Then the face class is modelled using a small training set and the nonface class is defined by choosing nonface images that lie close to the face class. Finally, the SVM classifier together with Bayesian statistical analysis procedure applies the efficient features defined by DFA for face and nonface classification. Experiments using both still images and video streams show the feasibility of our new face detection method. In particular, when using 92 images (containing 282 faces) from the MIT-CMU test sets, our method achieves 98.2% correct face detection accuracy with 2 false detections. When using video streams, our method detects faces reliably with computational efficiency of more than 20 frames per second.
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U2 - 10.1109/ICPR.2004.1334236
DO - 10.1109/ICPR.2004.1334236
M3 - Conference contribution
AN - SCOPUS:10044254244
SN - 0769521282
T3 - Proceedings - International Conference on Pattern Recognition
SP - 407
EP - 410
BT - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
A2 - Kittler, J.
A2 - Petrou, M.
A2 - Nixon, M.
T2 - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Y2 - 23 August 2004 through 26 August 2004
ER -